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Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors
| Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 511.8 |
| Soggetto topico |
Mathematical models
Simulation methods Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10193-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Biography -- Contents -- Monte Carlo Methods for Pricing American Options -- 1 Introduction -- 2 American Option Pricing -- 3 Binomial Tree Method -- 4 Dynamic Programming Approach -- 4.1 Regression Methods -- 4.2 Malliavin Calculus -- 5 Control Variates -- 6 Numerical Experiments -- 7 Conclusion -- References -- Remarks on Lévy Process Simulation -- 1 Introduction -- 2 Lévy Processes -- 3 Main Examples -- 4 The ε-Algorithm -- 5 Using Complete Monotonicity Structure -- 6 Numerical Examples -- 7 Exact Simulation of X(h) and other Methods -- 8 Maxima, Minima and Other Path Functionals -- References -- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes -- 1 Introduction -- 2 Basic Strategy -- 2.1 Milestone Events -- 2.2 Main Algorithm -- 3 Intermediate Steps in Algorithm 2 -- 4 Analysis of Algorithm 2 -- 4.1 Output Analysis -- 4.2 Complexity Analysis -- 5 Numerical Experiments -- 6 Conclusion -- References -- Truncated Multivariate Student Computations via Exponential Tilting -- 1 Introduction -- 2 Review of the Sequentially Tilted Proposal Density -- 3 Asymptotic Efficiency of the IS Estimator -- 4 Application to Constrained Linear Regression -- 5 Tobit Model Application -- 6 Application to ``Bayesian'' Splines for Non-negative Functions -- 7 The Reject-Regenerate Sampler -- 7.1 Nummelin Splitting of Transition Kernel -- 7.2 Rare-Event Robustness -- 8 Concluding Remarks -- References -- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints -- 1 Introduction -- 2 Classical Portfolio Selection in a Nutshell -- 3 Portfolio Optimization with Many Constraints -- 4 Approximation of the Opportunity Set by Naïve Monte Carlo, and by Exponential Monte Carlo -- 5 Approximation of the Opportunity Set with Exponential QMC.
6 Approximating the Market Portfolio with MC, Exponential MC, and Exponential QMC -- 7 Approximating the Whole OS with MC, Exponential MC, and Exponential QMC -- 8 How to Calculate the Dispersion of a Sample Set in an OS? -- 9 Some Simulation Results -- 10 Conclusions, Outlook, and Further Practical Problem -- References -- Geometric-Moment Contraction of G/G/1 Waiting Times -- 1 Introduction -- 2 Main Results -- 3 Monte Carlo Results -- 3.1 M/M/1 Queue -- 3.2 M/G/1 Queues -- 4 Conclusions -- References -- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting -- 1 Introduction -- 2 Basic Definitions -- 2.1 Function Space Setting -- 2.2 Approximation in script upper H Subscript d comma alpha comma bold italic gammamathcalHd,α,γ -- 2.3 The Worst-Case Setting -- 2.4 Useful Relations -- 2.5 Relations to the Average-Case Setting -- 2.6 Notions of Tractability -- 3 The Results for normal upper A normal upper P normal upper P Subscript 2APP2 -- 4 The Results for normal upper A normal upper P normal upper P Subscript normal infinityAPPinfty -- 5 Overview and Formulation of Open Problems -- 5.1 Open Problems -- References -- Rare-Event Simulation via Neural Networks -- 1 Introduction -- 1.1 Background -- 2 Rare-Event Deep Learning -- 2.1 Networks and Loss Functions -- 2.2 Kernel Density Estimation -- 2.3 Training Procedure -- 2.4 Rare-Event Distribution -- 3 Experimental Results -- 3.1 Learning Normal Distributions -- 3.2 Normal Distribution Rare-Events -- 3.3 Learning Sum of Exponential Distributions -- 4 Conclusions and Further Research -- References -- Preintegration is Not Smoothing When Monotonicity Fails -- 1 Introduction -- 1.1 Related Work -- 1.2 The Problem -- 1.3 Informative Examples -- 1.4 Outline of This Paper -- 2 Smoothness Theorems in dd Dimensions -- 3 A High-Dimensional Example -- 4 Conclusion -- References. Combined Derivative Estimators -- 1 Introduction -- 2 Derivative Estimation -- 2.1 Background -- 2.2 Combined Estimators -- 2.3 Second Derivatives -- 2.4 Finite Difference Estimators and IPA -- 2.5 IPA and Randomized Score Functions -- 2.6 LRM Singularities -- 2.7 Generalized Likelihood Ratio Method -- 3 A Barrier Option Example -- 3.1 The Option Pricing Setting -- 3.2 The Barrier Option -- 3.3 A Combined IPA-LRM Estimator of Wang et al. ch10wang -- 3.4 GLR as a Combined IPA-LRM Estimator -- 4 Approaching Continuous Time: Averaging Low-Rank GLR Estimators -- 4.1 Approximating Continuous-Time Sensitivities -- 4.2 Averaging GLR Estimators -- 5 Concluding Remarks -- References -- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting -- 1 Introduction -- 2 A Quantile Central Limit Theorem -- 3 A Uniform CLT for 1-Dependent Sequences -- 4 A Quantile Central Limit Theorem for Harris Processes -- 5 The Validity of Non-overlapping Batch-Means Estimation -- 6 Sufficient Conditions -- References -- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling -- 1 Introduction -- 2 Markov Chain Simulation with Stratified Sampling -- 2.1 Classical Monte Carlo -- 2.2 Simple Stratified Sampling -- 2.3 Sudoku Latin Square Sampling -- 3 Variance Bounds -- 3.1 Classical Monte Carlo -- 3.2 Simple Stratified Sampling -- 3.3 Sudoku Latin Square Sampling -- 4 Numerical Experiments -- 4.1 An Autoregressive Process -- 4.2 A European Put Option -- 4.3 Diffusion -- 5 Conclusions -- References -- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs -- 1 Introduction -- 2 Methods for the Black Box Setting -- 2.1 Methods Based on the Empirical Quantile Function -- 2.2 Methods Based on a Generalized Pareto Approximation in the Tail -- 3 Combining AR with RQMC. 4 Application: Basket Option Pricing -- 5 Conclusion -- References -- A Generalized Transformed Density Rejection Algorithm -- 1 Introduction -- 2 Transformed Density Rejection with Inflection Points -- 3 Determine Signs of Second Derivatives -- 3.1 Initial Intervals -- 3.2 Splitting Intervals -- 4 The Algorithm -- 5 Applications -- 5.1 Generalized Hyperbolic Distribution -- 5.2 Truncated Distributions -- 5.3 Watson Distributions -- 6 Conclusions -- References -- Fast Automatic Bayesian Cubature Using Sobol' Sampling -- 1 Introduction -- 2 Bayesian Cubature -- 3 Digital Nets and Walsh Kernels -- 3.1 Digital Sequences -- 3.2 Covariance Kernels Constructed Via Walsh Functions -- 3.3 Eigenvector-Eigenvalue Decomposition of the Gram Matrix -- 4 Numerical Experiments -- 4.1 Multivariate Gaussian Probability -- 4.2 Keister's Example -- 4.3 Asian Option Pricing -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Rendering Along the Hilbert Curve -- 1 Introduction -- 2 Visual Error in Image Synthesis -- 3 Enumerating Pixels Along the Hilbert Curve -- 3.1 Correlation in Space-Filling Curves -- 3.2 Blue-Noise Dithered Sampling -- 4 Progressive Image Synthesis -- 4.1 Deterministic Cranley-Patterson Rotation -- 4.2 Randomization -- 4.3 Contiguous Segments of one Low Discrepancy Sequence -- 4.4 Partitioning one Low Discrepancy Sequence -- 5 Results and Discussion -- 6 Conclusion -- References -- Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System -- 1 Introduction -- 2 Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set -- 2.1 Assumptions and Notations -- 2.2 Exponential Limit -- 2.3 Exponential Estimators with Monte Carlo (MC) -- 2.4 Convolution Estimators with Monte Carlo. 3 Array-RQMC Implementation of Regenerative-Simulation-Based Estimators of Quantiles -- 3.1 RQMC and Array-RQMC -- 3.2 Array-RQMC Exponential and Convolution Estimators -- 4 Numerical Illustration of the Gain on the Simulation of an M/M/1 Queue -- 5 Conclusions -- References -- Foundations of Ranking & -- Selection for Simulation Optimization -- 1 Introduction -- 2 Set Up -- 3 The Normal Means Case -- 3.1 The Indifference-Zone (IZ) Formulation -- 3.2 R& -- S Based on ``Statistical Learning'' -- 3.3 A Convergence-Rate Perspective -- 3.4 Doing Better Than ``Rate Optimal'' -- 3.5 Common Random Numbers -- 3.6 ``Good Selection'' -- 3.7 Unknown Variances -- 3.8 A Note on Asymptotic Analysis -- 4 Parallel R& -- S -- 4.1 New Measures of Efficiency -- 4.2 New Objectives -- 4.3 Parting Thoughts -- 5 Other Formulations -- 6 Multi-armed Bandits -- References -- Where are the Logs? -- 1 Introduction -- 2 Background -- 3 Proof of the Lower Bound -- 4 Discrepancy and the Case of d equals 1d=1 -- 5 Empirical Investigations for d equals 2d=2 -- 6 Very Large mm for Sobol' Nets -- 7 Discussion -- References -- Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo -- 1 Introduction -- 2 Performability Metrics and Resilience -- 2.1 The Resilience Metric -- 2.2 Some Properties of Resilience -- 3 Using Standard Monte Carlo for Resilience-Based Analysis -- 3.1 The Standard Estimator -- 3.2 The Standard Estimator Efficiently Implemented in the Rare Event Case -- 3.3 Estimating the Resilience -- 3.4 Improving Algorithm B -- 3.5 Sensitivity Analysis -- 4 Examples and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNINA-9910633937903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors
| Advances in modeling and simulation : festschrift for Pierre L'Ecuyer / / Zdravko Botev [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (426 pages) |
| Disciplina | 511.8 |
| Soggetto topico |
Mathematical models
Simulation methods Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-10193-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Biography -- Contents -- Monte Carlo Methods for Pricing American Options -- 1 Introduction -- 2 American Option Pricing -- 3 Binomial Tree Method -- 4 Dynamic Programming Approach -- 4.1 Regression Methods -- 4.2 Malliavin Calculus -- 5 Control Variates -- 6 Numerical Experiments -- 7 Conclusion -- References -- Remarks on Lévy Process Simulation -- 1 Introduction -- 2 Lévy Processes -- 3 Main Examples -- 4 The ε-Algorithm -- 5 Using Complete Monotonicity Structure -- 6 Numerical Examples -- 7 Exact Simulation of X(h) and other Methods -- 8 Maxima, Minima and Other Path Functionals -- References -- Exact Sampling for the Maximum of Infinite Memory Gaussian Processes -- 1 Introduction -- 2 Basic Strategy -- 2.1 Milestone Events -- 2.2 Main Algorithm -- 3 Intermediate Steps in Algorithm 2 -- 4 Analysis of Algorithm 2 -- 4.1 Output Analysis -- 4.2 Complexity Analysis -- 5 Numerical Experiments -- 6 Conclusion -- References -- Truncated Multivariate Student Computations via Exponential Tilting -- 1 Introduction -- 2 Review of the Sequentially Tilted Proposal Density -- 3 Asymptotic Efficiency of the IS Estimator -- 4 Application to Constrained Linear Regression -- 5 Tobit Model Application -- 6 Application to ``Bayesian'' Splines for Non-negative Functions -- 7 The Reject-Regenerate Sampler -- 7.1 Nummelin Splitting of Transition Kernel -- 7.2 Rare-Event Robustness -- 8 Concluding Remarks -- References -- Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints -- 1 Introduction -- 2 Classical Portfolio Selection in a Nutshell -- 3 Portfolio Optimization with Many Constraints -- 4 Approximation of the Opportunity Set by Naïve Monte Carlo, and by Exponential Monte Carlo -- 5 Approximation of the Opportunity Set with Exponential QMC.
6 Approximating the Market Portfolio with MC, Exponential MC, and Exponential QMC -- 7 Approximating the Whole OS with MC, Exponential MC, and Exponential QMC -- 8 How to Calculate the Dispersion of a Sample Set in an OS? -- 9 Some Simulation Results -- 10 Conclusions, Outlook, and Further Practical Problem -- References -- Geometric-Moment Contraction of G/G/1 Waiting Times -- 1 Introduction -- 2 Main Results -- 3 Monte Carlo Results -- 3.1 M/M/1 Queue -- 3.2 M/G/1 Queues -- 4 Conclusions -- References -- Tractability of Approximation in the Weighted Korobov Space in the Worst-Case Setting -- 1 Introduction -- 2 Basic Definitions -- 2.1 Function Space Setting -- 2.2 Approximation in script upper H Subscript d comma alpha comma bold italic gammamathcalHd,α,γ -- 2.3 The Worst-Case Setting -- 2.4 Useful Relations -- 2.5 Relations to the Average-Case Setting -- 2.6 Notions of Tractability -- 3 The Results for normal upper A normal upper P normal upper P Subscript 2APP2 -- 4 The Results for normal upper A normal upper P normal upper P Subscript normal infinityAPPinfty -- 5 Overview and Formulation of Open Problems -- 5.1 Open Problems -- References -- Rare-Event Simulation via Neural Networks -- 1 Introduction -- 1.1 Background -- 2 Rare-Event Deep Learning -- 2.1 Networks and Loss Functions -- 2.2 Kernel Density Estimation -- 2.3 Training Procedure -- 2.4 Rare-Event Distribution -- 3 Experimental Results -- 3.1 Learning Normal Distributions -- 3.2 Normal Distribution Rare-Events -- 3.3 Learning Sum of Exponential Distributions -- 4 Conclusions and Further Research -- References -- Preintegration is Not Smoothing When Monotonicity Fails -- 1 Introduction -- 1.1 Related Work -- 1.2 The Problem -- 1.3 Informative Examples -- 1.4 Outline of This Paper -- 2 Smoothness Theorems in dd Dimensions -- 3 A High-Dimensional Example -- 4 Conclusion -- References. Combined Derivative Estimators -- 1 Introduction -- 2 Derivative Estimation -- 2.1 Background -- 2.2 Combined Estimators -- 2.3 Second Derivatives -- 2.4 Finite Difference Estimators and IPA -- 2.5 IPA and Randomized Score Functions -- 2.6 LRM Singularities -- 2.7 Generalized Likelihood Ratio Method -- 3 A Barrier Option Example -- 3.1 The Option Pricing Setting -- 3.2 The Barrier Option -- 3.3 A Combined IPA-LRM Estimator of Wang et al. ch10wang -- 3.4 GLR as a Combined IPA-LRM Estimator -- 4 Approaching Continuous Time: Averaging Low-Rank GLR Estimators -- 4.1 Approximating Continuous-Time Sensitivities -- 4.2 Averaging GLR Estimators -- 5 Concluding Remarks -- References -- A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting -- 1 Introduction -- 2 A Quantile Central Limit Theorem -- 3 A Uniform CLT for 1-Dependent Sequences -- 4 A Quantile Central Limit Theorem for Harris Processes -- 5 The Validity of Non-overlapping Batch-Means Estimation -- 6 Sufficient Conditions -- References -- Simulation of Markov Chains with Continuous State Space by Using Simple Stratified and Sudoku Latin Square Sampling -- 1 Introduction -- 2 Markov Chain Simulation with Stratified Sampling -- 2.1 Classical Monte Carlo -- 2.2 Simple Stratified Sampling -- 2.3 Sudoku Latin Square Sampling -- 3 Variance Bounds -- 3.1 Classical Monte Carlo -- 3.2 Simple Stratified Sampling -- 3.3 Sudoku Latin Square Sampling -- 4 Numerical Experiments -- 4.1 An Autoregressive Process -- 4.2 A European Put Option -- 4.3 Diffusion -- 5 Conclusions -- References -- Quasi-Random Sampling with Black Box or Acceptance-Rejection Inputs -- 1 Introduction -- 2 Methods for the Black Box Setting -- 2.1 Methods Based on the Empirical Quantile Function -- 2.2 Methods Based on a Generalized Pareto Approximation in the Tail -- 3 Combining AR with RQMC. 4 Application: Basket Option Pricing -- 5 Conclusion -- References -- A Generalized Transformed Density Rejection Algorithm -- 1 Introduction -- 2 Transformed Density Rejection with Inflection Points -- 3 Determine Signs of Second Derivatives -- 3.1 Initial Intervals -- 3.2 Splitting Intervals -- 4 The Algorithm -- 5 Applications -- 5.1 Generalized Hyperbolic Distribution -- 5.2 Truncated Distributions -- 5.3 Watson Distributions -- 6 Conclusions -- References -- Fast Automatic Bayesian Cubature Using Sobol' Sampling -- 1 Introduction -- 2 Bayesian Cubature -- 3 Digital Nets and Walsh Kernels -- 3.1 Digital Sequences -- 3.2 Covariance Kernels Constructed Via Walsh Functions -- 3.3 Eigenvector-Eigenvalue Decomposition of the Gram Matrix -- 4 Numerical Experiments -- 4.1 Multivariate Gaussian Probability -- 4.2 Keister's Example -- 4.3 Asian Option Pricing -- 4.4 Discussion -- 5 Conclusion and Future Work -- References -- Rendering Along the Hilbert Curve -- 1 Introduction -- 2 Visual Error in Image Synthesis -- 3 Enumerating Pixels Along the Hilbert Curve -- 3.1 Correlation in Space-Filling Curves -- 3.2 Blue-Noise Dithered Sampling -- 4 Progressive Image Synthesis -- 4.1 Deterministic Cranley-Patterson Rotation -- 4.2 Randomization -- 4.3 Contiguous Segments of one Low Discrepancy Sequence -- 4.4 Partitioning one Low Discrepancy Sequence -- 5 Results and Discussion -- 6 Conclusion -- References -- Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System -- 1 Introduction -- 2 Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set -- 2.1 Assumptions and Notations -- 2.2 Exponential Limit -- 2.3 Exponential Estimators with Monte Carlo (MC) -- 2.4 Convolution Estimators with Monte Carlo. 3 Array-RQMC Implementation of Regenerative-Simulation-Based Estimators of Quantiles -- 3.1 RQMC and Array-RQMC -- 3.2 Array-RQMC Exponential and Convolution Estimators -- 4 Numerical Illustration of the Gain on the Simulation of an M/M/1 Queue -- 5 Conclusions -- References -- Foundations of Ranking & -- Selection for Simulation Optimization -- 1 Introduction -- 2 Set Up -- 3 The Normal Means Case -- 3.1 The Indifference-Zone (IZ) Formulation -- 3.2 R& -- S Based on ``Statistical Learning'' -- 3.3 A Convergence-Rate Perspective -- 3.4 Doing Better Than ``Rate Optimal'' -- 3.5 Common Random Numbers -- 3.6 ``Good Selection'' -- 3.7 Unknown Variances -- 3.8 A Note on Asymptotic Analysis -- 4 Parallel R& -- S -- 4.1 New Measures of Efficiency -- 4.2 New Objectives -- 4.3 Parting Thoughts -- 5 Other Formulations -- 6 Multi-armed Bandits -- References -- Where are the Logs? -- 1 Introduction -- 2 Background -- 3 Proof of the Lower Bound -- 4 Discrepancy and the Case of d equals 1d=1 -- 5 Empirical Investigations for d equals 2d=2 -- 6 Very Large mm for Sobol' Nets -- 7 Discussion -- References -- Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo -- 1 Introduction -- 2 Performability Metrics and Resilience -- 2.1 The Resilience Metric -- 2.2 Some Properties of Resilience -- 3 Using Standard Monte Carlo for Resilience-Based Analysis -- 3.1 The Standard Estimator -- 3.2 The Standard Estimator Efficiently Implemented in the Rare Event Case -- 3.3 Estimating the Resilience -- 3.4 Improving Algorithm B -- 3.5 Sensitivity Analysis -- 4 Examples and Discussions -- 5 Conclusions -- References. |
| Record Nr. | UNISA-996499866203316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational intelligence : 11th international joint conference, IJCCI 2019, Vienna, Austria, September 17-19, 2019, revised selected papers / / Juan Julian Merelo [and four others], editors
| Computational intelligence : 11th international joint conference, IJCCI 2019, Vienna, Austria, September 17-19, 2019, revised selected papers / / Juan Julian Merelo [and four others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (414 pages) |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence - Simulation methods
Artificial intelligence Intel·ligència computacional Mètodes de simulació Intel·ligència artificial |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-70594-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Evolutionary Computation Theory and Applications -- Niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Scheduling -- 1 Introduction -- 2 Related Work -- 2.1 Routing and Sequencing Rules -- 2.2 Feature Selection -- 3 Background -- 3.1 Problem Definition -- 3.2 Genetic Programming for Dynamic Job Shop Scheduling -- 3.3 Multi-tree Genetic Programming -- 3.4 Niching-GP Feature Selection -- 3.5 Two-stage Genetic Programming with Feature Selection -- 4 Methods -- 4.1 Feature Selection for Multi-tree Genetic Programming -- 4.2 Two-Stage Multi-tree Genetic Programming -- 5 Experimental Setup -- 5.1 Scenario Generation Configuration -- 5.2 GP Configuration -- 5.3 Method Comparison -- 6 Experimental Results -- 7 Result Discussion -- 8 Conclusion -- 9 Future Work -- References -- Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics -- 1 Introduction -- 2 Timing Buy and Sell Decisions -- 3 Related Work -- 4 Trend Representative Testing: Simulating Various Market Conditions While Training and Testing -- 5 Market Timing Algorithms -- 5.1 Individual Encoding and Measuring Fitness -- 5.2 Genetic Algorithms -- 5.3 Particle Swarm Optimization -- 6 Experimental Setup -- 7 Results -- 8 Conclusion -- References -- Hybrid Strategy Coupling EGO and CMA-ES for Structural Topology Optimization in Statics and Crashworthiness -- 1 Introduction -- 2 Problem Representation -- 2.1 Parametrization -- 2.2 Geometry Mapping -- 3 Optimization Problem and Constraints -- 4 Resolution Strategy -- 4.1 Optimization Algorithm -- 4.2 Constraint Handling Techniques -- 5 Test Case -- 5.1 Linear Elastic Case -- 5.2 Nonlinear Crash Case -- 6 Experimental Setup -- 7 Results -- 7.1 9-Variables Linear Elastic Case.
7.2 15-Variables Test Cases -- 8 Conclusions -- References -- An Empirical Study on Insertion and Deletion Mutation in Cartesian Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Cartesian Genetic Programming -- 2.2 Advanced Mutation Techniques in Standard CGP -- 3 Insertion and Deletion Mutation in CGP -- 3.1 The Insertion Mutation Technique -- 3.2 The Deletion Mutation Technique -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Search Performance Evaluation -- 4.3 Fitness Range Analysis -- 4.4 Active Function Node Range Analysis -- 5 Comparison to EGGP -- 6 Discussion -- 7 Conclusion -- 8 Future Work -- References -- Handling Complexity in Some Typical Problems of Distributed Systems by Using Self-organizing Principles -- 1 Introduction -- 1.1 Self-organization -- 1.2 Complexity in Application Scenarios -- 1.3 Measurement of Complexity -- 2 Swarm Intelligence in Distributed Systems -- 2.1 Some Selected Distributed Systems' Use-Cases -- 2.2 Algorithm Recommendation for Selected Use Cases -- 3 An Illustration: Bee Algorithm for Dynamic Load Balancing -- 3.1 Bee Algorithm -- 3.2 P2P Network Model -- 3.3 Convergence -- 4 Conclusion -- References -- Fuzzy Computation Theory and Applications -- Markov Decision Processes with Fuzzy Risk-Sensitive Rewards: The Best Coherent Risk Measures Under Risk Averse Utilities -- 1 Introduction -- 2 Coherent Risk Measures Derived from Risk Averse Utilities -- 3 Fuzziness and Extended Criteria -- 4 Estimation of Fuzziness with Evaluation Weights and θ-mean Functions -- 5 Markov Decision with Risk Allocation by Coherent Risk Measures -- 6 Maximization of Risk-Sensitive Running Rewards Under Feasible Risk Constraints -- 7 Maximization of Risk-Sensitive Terminal Rewards Under Feasible Risk Constraints -- 8 Numerical Examples -- 9 Conclusion -- References. Correlation Analysis Via Intuitionistic Fuzzy Modal and Aggregation Operators -- 1 Introduction -- 1.1 The Relevance for Contextualizing A-CC -- 1.2 Main Contribution -- 1.3 Paper Outline -- 2 Related Works -- 3 Preliminary -- 3.1 Intuitionistic Fuzzy Negations -- 3.2 Intuitionistic Fuzzy T-norms and T-conorms -- 3.3 Intuitionistic Fuzzy Modal Operators -- 3.4 Intuitionistic Fuzzy α-level Modal Operators -- 3.5 Action of Conjugate Operators on Aggregation Operators -- 4 Correlation from A-IFL -- 5 Results on Conjugate Modal Operators -- 6 A-CC Results on Modal Operators -- 7 A-CC Results α-Level Modal Operators -- 8 A-CC Results on Triangular (Co)Norms and Modal Operators -- 9 Conclusion and Further Work -- References -- Fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interaction -- 1 Introduction -- 2 Gaussian Noise and the Intersection Problem -- 2.1 Computation of Intersections-Analytical Approach -- 2.2 Transformation of Gaussian Distributions -- 3 Inverse Solution -- 4 Fuzzy Solution -- 5 Extension to Six Inputs and Two Outputs -- 5.1 General Approach -- 5.2 Fuzzy Approach -- 5.3 The Energetic Approach -- 6 Mixed Gaussian Distributions -- 7 Robots and Humans in Motion -- 8 Simulation Results -- 9 Conclusions -- References -- Predicting Cardiovascular Death with Automatically Designed Fuzzy Logic Rule-Based Models -- 1 Introduction -- 2 Evolutionary Fuzzy Logic Rule-Based Predictive Modeling -- 3 Data Description -- 4 Experiments and Results -- 5 Conclusions -- References -- Neural Computation Theory and Applications -- Neural Models to Quantify the Determinants of Truck Fuel Consumption -- 1 Introduction -- 2 Collection of Fuel Consumption and Input Factor Data -- 3 Extracting Statistics for Route and Driver Fuel Economy -- 4 Extracting Empirical Fuel Economy Models. 5 Estimating Model Compensation Impact on Driver Performance Measurements -- 6 Extracting Statistics for Fuel Shrinkage -- 7 Extracting Empirical Fuel Shrinkage Models -- 8 Conclusions and Future Work -- References -- Towards a Class-Aware Information Granulation for Graph Embedding and Classification -- 1 Introduction -- 2 Embedding via Data Granulation -- 3 The GRALG Classification System -- 3.1 Extractor -- 3.2 Granulator -- 3.3 Embedder -- 3.4 Classifier -- 3.5 Training Phase -- 3.6 Synthesized Classification Model and Test Phase -- 4 Extractor and Granulation Improvements -- 4.1 Class-Aware Extractor -- 4.2 Class-Aware Granulator -- 4.3 Class-Aware Granulator with Uniform Q Scaling -- 4.4 Class-Aware Granulator with Frequency-Based Q Scaling -- 5 Test and Results -- 6 Conclusions -- References -- Near Optimal Solving of the (N2-1)-puzzle Using Heuristics Based on Artificial Neural Networks -- 1 Introduction -- 1.1 Contributions -- 2 Background -- 2.1 The (N2-1)-puzzle -- 2.2 Artificial Neural Networks -- 3 Related Work -- 4 Designing a New Heuristic -- 4.1 Encoding the Input and Output -- 4.2 Design of the Neural Networks -- 4.3 Training Data and Training -- 4.4 Resulting ANN-distance Heuristics -- 5 Experimental Evaluation -- 5.1 Evaluation on Single Estimations -- 5.2 Evaluation on A* Searches -- 5.3 Competitive Comparison Against Heuristics Presented in Other Studies -- 5.4 Analysis of the Behavior of A* Search with the Underlying ANN-distance Heuristic -- 6 Discussion and Conclusion -- References -- Deep Convolutional Neural Network Processing of Images for Obstacle Avoidance -- 1 Introduction -- 2 Deep Learning for Image Processing -- 2.1 Components of a Deep Learning System -- 2.2 Relevant Previous Works -- 3 Obstacle Avoidance Task -- 3.1 The Robot -- 3.2 The Environment -- 3.3 Relevant Previous Works. 4 Deep Learning Applied to Obstacle Avoidance -- 4.1 Data Collection -- 4.2 Deep Learning Application -- 5 Results -- 5.1 Robot Performance in the Environment -- 5.2 Examining Network Weights and Activations -- 6 Conclusions -- References -- CVaR Q-Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Conditional Value-at-Risk -- 2.2 Q-Learning -- 2.3 Distributional Transition Operator -- 2.4 Problem Formulation -- 3 CVaR Value Iteration -- 3.1 Bellman Equation for CVaR -- 3.2 CVaR Value Iteration with Linear Interpolation -- 3.3 Accelerated Value Iteration for CVaR -- 3.4 Computing ξ -- 3.5 Experiments -- 4 CVaR Q-Learning -- 4.1 Estimating CVaR -- 4.2 Temporal Difference Updates -- 4.3 CVaR and Policy Improvement -- 4.4 CVaR Q-Learning with VaR-Based Policy Improvement -- 4.5 Experiments -- 5 Deep CVaR Q-Learning -- 5.1 Loss Functions -- 5.2 Experiments -- 6 Conclusion -- A Proofs of Theoretical Results -- A.1 Proof of Theorem 1 -- A.2 Proof of Theorem 2 -- B Other Results -- B.1 CVaR Value Iteration -Linear Program -- References -- Rule Extraction from Neural Networks and Other Classifiers Applied to XSS Detection -- 1 Introduction -- 2 Background and Related Work -- 2.1 Overview of Minimising Boolean Expressions -- 2.2 Cross-Site Scripting -- 3 Methodology -- 3.1 Datasets -- 3.2 Selected Features -- 3.3 Training Classifiers -- 3.4 Classifiers and Boolean Functions -- 3.5 Sampling -- 3.6 Extracting Rules -- 4 Results -- 4.1 Neural Networks -- 4.2 Support Vector Machines -- 4.3 k-NN -- 4.4 Timings -- 4.5 Labelling via Sampling -- 5 Discussion -- 6 Conclusion -- References -- Introduction to Sequential Heteroscedastic Probabilistic Neural Networks -- 1 Introduction -- 2 A Review of RHPNN -- 3 Derivation of SHPNN Formulation -- 4 The SHPNN Algorithm -- 5 Results -- 6 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910488702003321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Financial risk forecasting [[electronic resource] ] : the theory and practice of forecasting market risk, with implementation in R and Matlab / / Jón Daníelsson
| Financial risk forecasting [[electronic resource] ] : the theory and practice of forecasting market risk, with implementation in R and Matlab / / Jón Daníelsson |
| Autore | Daníelsson Jón |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (298 p.) |
| Disciplina |
658.155
658.1550112 |
| Collana | Wiley finance series |
| Soggetto topico |
Financial risk management - Forecasting
Financial risk management - Simulation methods R (Computer program language) Gestió financera Gestió del risc Previsió Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-119-97711-8
1-119-20586-7 1-283-40512-1 9786613405128 1-119-97710-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Financial Risk Forecasting; Contents; Preface; Acknowledgments; Abbreviations; Notation; 1 Financial markets, prices and risk; 1.1 Prices, returns and stock indices; 1.1.1 Stock indices; 1.1.2 Prices and returns; 1.2 S&P 500 returns; 1.2.1 S&P 500 statistics; 1.2.2 S&P 500 statistics in R and Matlab; 1.3 The stylized facts of financial returns; 1.4 Volatility; 1.4.1 Volatility clusters; 1.4.2 Volatility clusters and the ACF; 1.5 Nonnormality and fat tails; 1.6 Identification of fat tails; 1.6.1 Statistical tests for fat tails; 1.6.2 Graphical methods for fat tail analysis
1.6.3 Implications of fat tails in finance1.7 Nonlinear dependence; 1.7.1 Sample evidence of nonlinear dependence; 1.7.2 Exceedance correlations; 1.8 Copulas; 1.8.1 The Gaussian copula; 1.8.2 The theory of copulas; 1.8.3 An application of copulas; 1.8.4 Some challenges in using copulas; 1.9 Summary; 2 Univariate volatility modeling; 2.1 Modeling volatility; 2.2 Simple volatility models; 2.2.1 Moving average models; 2.2.2 EWMA model; 2.3 GARCH and conditional volatility; 2.3.1 ARCH; 2.3.2 GARCH; 2.3.3 The ''memory'' of a GARCH model; 2.3.4 Normal GARCH; 2.3.5 Student-t GARCH 2.3.6 (G)ARCH in mean2.4 Maximum likelihood estimation of volatility models; 2.4.1 The ARCH(1) likelihood function; 2.4.2 The GARCH(1,1) likelihood function; 2.4.3 On the importance of σ1; 2.4.4 Issues in estimation; 2.5 Diagnosing volatility models; 2.5.1 Likelihood ratio tests and parameter significance; 2.5.2 Analysis of model residuals; 2.5.3 Statistical goodness-of-fit measures; 2.6 Application of ARCH and GARCH; 2.6.1 Estimation results; 2.6.2 Likelihood ratio tests; 2.6.3 Residual analysis; 2.6.4 Graphical analysis; 2.6.5 Implementation; 2.7 Other GARCH-type models 2.7.1 Leverage effects and asymmetry2.7.2 Power models; 2.7.3 APARCH; 2.7.4 Application of APARCH models; 2.7.5 Estimation of APARCH; 2.8 Alternative volatility models; 2.8.1 Implied volatility; 2.8.2 Realized volatility; 2.8.3 Stochastic volatility; 2.9 Summary; 3 Multivariate volatility models; 3.1 Multivariate volatility forecasting; 3.1.1 Application; 3.2 EWMA; 3.3 Orthogonal GARCH; 3.3.1 Orthogonalizing covariance; 3.3.2 Implementation; 3.3.3 Large-scale implementations; 3.4 CCC and DCC models; 3.4.1 Constant conditional correlations (CCC); 3.4.2 Dynamic conditional correlations (DCC) 3.4.3 Implementation3.5 Estimation comparison; 3.6 Multivariate extensions of GARCH; 3.6.1 Numerical problems; 3.6.2 The BEKK model; 3.7 Summary; 4 Risk measures; 4.1 Defining and measuring risk; 4.2 Volatility; 4.3 Value-at-risk; 4.3.1 Is VaR a negative or positive number?; 4.3.2 The three steps in VaR calculations; 4.3.3 Interpreting and analyzing VaR; 4.3.4 VaR and normality; 4.3.5 Sign of VaR; 4.4 Issues in applying VaR; 4.4.1 VaR is only a quantile; 4.4.2 Coherence; 4.4.3 Does VaR really violate subadditivity?; 4.4.4 Manipulating VaR; 4.5 Expected shortfall 4.6 Holding periods, scaling and the square root of time |
| Record Nr. | UNINA-9910139552503321 |
Daníelsson Jón
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| Chichester, West Sussex, U.K., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Financial risk forecasting : the theory and practice of forecasting market risk, with implementation in R and Matlab / / Jón Daníelsson
| Financial risk forecasting : the theory and practice of forecasting market risk, with implementation in R and Matlab / / Jón Daníelsson |
| Autore | Daníelsson Jón |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (298 p.) |
| Disciplina |
658.155
658.1550112 |
| Collana | Wiley finance series |
| Soggetto topico |
Financial risk management - Forecasting
Financial risk management - Simulation methods R (Computer program language) Gestió financera Gestió del risc Previsió Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
1-119-97711-8
1-119-20586-7 1-283-40512-1 9786613405128 1-119-97710-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Financial Risk Forecasting; Contents; Preface; Acknowledgments; Abbreviations; Notation; 1 Financial markets, prices and risk; 1.1 Prices, returns and stock indices; 1.1.1 Stock indices; 1.1.2 Prices and returns; 1.2 S&P 500 returns; 1.2.1 S&P 500 statistics; 1.2.2 S&P 500 statistics in R and Matlab; 1.3 The stylized facts of financial returns; 1.4 Volatility; 1.4.1 Volatility clusters; 1.4.2 Volatility clusters and the ACF; 1.5 Nonnormality and fat tails; 1.6 Identification of fat tails; 1.6.1 Statistical tests for fat tails; 1.6.2 Graphical methods for fat tail analysis
1.6.3 Implications of fat tails in finance1.7 Nonlinear dependence; 1.7.1 Sample evidence of nonlinear dependence; 1.7.2 Exceedance correlations; 1.8 Copulas; 1.8.1 The Gaussian copula; 1.8.2 The theory of copulas; 1.8.3 An application of copulas; 1.8.4 Some challenges in using copulas; 1.9 Summary; 2 Univariate volatility modeling; 2.1 Modeling volatility; 2.2 Simple volatility models; 2.2.1 Moving average models; 2.2.2 EWMA model; 2.3 GARCH and conditional volatility; 2.3.1 ARCH; 2.3.2 GARCH; 2.3.3 The ''memory'' of a GARCH model; 2.3.4 Normal GARCH; 2.3.5 Student-t GARCH 2.3.6 (G)ARCH in mean2.4 Maximum likelihood estimation of volatility models; 2.4.1 The ARCH(1) likelihood function; 2.4.2 The GARCH(1,1) likelihood function; 2.4.3 On the importance of σ1; 2.4.4 Issues in estimation; 2.5 Diagnosing volatility models; 2.5.1 Likelihood ratio tests and parameter significance; 2.5.2 Analysis of model residuals; 2.5.3 Statistical goodness-of-fit measures; 2.6 Application of ARCH and GARCH; 2.6.1 Estimation results; 2.6.2 Likelihood ratio tests; 2.6.3 Residual analysis; 2.6.4 Graphical analysis; 2.6.5 Implementation; 2.7 Other GARCH-type models 2.7.1 Leverage effects and asymmetry2.7.2 Power models; 2.7.3 APARCH; 2.7.4 Application of APARCH models; 2.7.5 Estimation of APARCH; 2.8 Alternative volatility models; 2.8.1 Implied volatility; 2.8.2 Realized volatility; 2.8.3 Stochastic volatility; 2.9 Summary; 3 Multivariate volatility models; 3.1 Multivariate volatility forecasting; 3.1.1 Application; 3.2 EWMA; 3.3 Orthogonal GARCH; 3.3.1 Orthogonalizing covariance; 3.3.2 Implementation; 3.3.3 Large-scale implementations; 3.4 CCC and DCC models; 3.4.1 Constant conditional correlations (CCC); 3.4.2 Dynamic conditional correlations (DCC) 3.4.3 Implementation3.5 Estimation comparison; 3.6 Multivariate extensions of GARCH; 3.6.1 Numerical problems; 3.6.2 The BEKK model; 3.7 Summary; 4 Risk measures; 4.1 Defining and measuring risk; 4.2 Volatility; 4.3 Value-at-risk; 4.3.1 Is VaR a negative or positive number?; 4.3.2 The three steps in VaR calculations; 4.3.3 Interpreting and analyzing VaR; 4.3.4 VaR and normality; 4.3.5 Sign of VaR; 4.4 Issues in applying VaR; 4.4.1 VaR is only a quantile; 4.4.2 Coherence; 4.4.3 Does VaR really violate subadditivity?; 4.4.4 Manipulating VaR; 4.5 Expected shortfall 4.6 Holding periods, scaling and the square root of time |
| Record Nr. | UNINA-9910811774203321 |
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Daníelsson Jón
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| Chichester, West Sussex, U.K., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Learning to diagnose with simulations : examples from teacher education and medical education / / Frank Fischer, Ansgar Opitz, editors
| Learning to diagnose with simulations : examples from teacher education and medical education / / Frank Fischer, Ansgar Opitz, editors |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (vi, 157 pages) : illustrations (some color) |
| Altri autori (Persone) |
FischerFrank
OpitzAnsgar |
| Soggetto topico |
Diagnosis - Simulation methods
Educational psychology Teaching of a specific subject Teacher training Industrial or vocational training Diagnòstic Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Open Access
Learning diagnostic competences with simulations Diagnosing primary school children’s mathematical competences Mathematical argumentation skills in secondary school Instructional quality of biology lessons Students’ behavioral, developmental and learning disorders |
| ISBN | 3-030-89147-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. A theoretical framework for learning diagnostic competences with simulations -- 2. Diagnosing primary school children’s mathematical competences and misconceptions based on their written work -- 3. Diagnosing mathematical argumentation skills in secondary school based on videos of students attempting to construct proofs -- 4. Diagnosing 6th graders' understanding of decimal fractions – A role-play-based simulation for mathematics teacher students of diagnostic interviews featuring simulated teacher-student interactions -- 5. Diagnosing the instructional quality of biology lessons based on staged-videos -- 6. Diagnosing secondary school students' scientific reasoning skills in physics and biology – A video-based simulation for pre-service teachers -- 7. Diagnosing students' behavioral, developmental and learning disorders based on records of behavior and performance -- 8. Facilitating Medical History Taking through Live and Computer Simulations -- 9. Learning collaborative Diagnosing in Medical Education : Diagnosing a Patient’s Disease in Collaboration with a Simulated Radiologist -- 10. Using simulations to facilitate professional competences: promising trajectories for future research. |
| Record Nr. | UNINA-9910544852803321 |
| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling Biomaterials [[electronic resource] /] / edited by Josef Málek, Endre Süli
| Modeling Biomaterials [[electronic resource] /] / edited by Josef Málek, Endre Süli |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2021 |
| Descrizione fisica | 1 online resource (281 pages) |
| Disciplina | 610.28 |
| Collana | Nečas Center Series |
| Soggetto topico |
Mathematical models
Stochastic models Markov processes Numerical analysis Continuum mechanics Biomaterials Mathematical Modeling and Industrial Mathematics Stochastic Modelling Markov Process Numerical Analysis Continuum Mechanics Materials biomèdics Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-88084-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Farago, O., A Beginner's Short Guide to Membrane Biophysics -- Misailidis, G., Ferenc, J., Tsiairis, C., Self-Organization of Tissues through Biochemical and Mechanical Signals -- Righi, M., Balbi, V., Foundations of Viscoelasticity and Application to Soft Tissue Mechanics -- Klika, V., Modeling of Biomaterials as an Application of the Theory of Mixtures -- Miller, R., et al., Modeling Biomechanics in the Healthy and Diseased Heart -- Chabiniok, R., et al., Translational Cardiovascular Modeling: Tetralogy of Fallot and Modeling of Diseases. |
| Record Nr. | UNISA-996466389703316 |
| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modeling Biomaterials / / edited by Josef Málek, Endre Süli
| Modeling Biomaterials / / edited by Josef Málek, Endre Süli |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2021 |
| Descrizione fisica | 1 online resource (281 pages) |
| Disciplina | 610.28 |
| Collana | Nečas Center Series |
| Soggetto topico |
Mathematical models
Stochastic models Markov processes Numerical analysis Continuum mechanics Biomaterials Mathematical Modeling and Industrial Mathematics Stochastic Modelling Markov Process Numerical Analysis Continuum Mechanics Materials biomèdics Models matemàtics Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-88084-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Farago, O., A Beginner's Short Guide to Membrane Biophysics -- Misailidis, G., Ferenc, J., Tsiairis, C., Self-Organization of Tissues through Biochemical and Mechanical Signals -- Righi, M., Balbi, V., Foundations of Viscoelasticity and Application to Soft Tissue Mechanics -- Klika, V., Modeling of Biomaterials as an Application of the Theory of Mixtures -- Miller, R., et al., Modeling Biomechanics in the Healthy and Diseased Heart -- Chabiniok, R., et al., Translational Cardiovascular Modeling: Tetralogy of Fallot and Modeling of Diseases. |
| Record Nr. | UNINA-9910520063803321 |
| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Probabilità, Statistica e Simulazione [[electronic resource] ] : Programmi applicativi scritti in R / / by Alberto Rotondi, Paolo Pedroni, Antonio Pievatolo
| Probabilità, Statistica e Simulazione [[electronic resource] ] : Programmi applicativi scritti in R / / by Alberto Rotondi, Paolo Pedroni, Antonio Pievatolo |
| Autore | Rotondi Alberto |
| Edizione | [4th ed. 2021.] |
| Pubbl/distr/stampa | Milano : , : Springer Milan : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XV, 621 pagg. 130 figg., 12 figg. a colori.) |
| Disciplina | 519 |
| Collana | La Matematica per il 3+2 |
| Soggetto topico |
Statistics
Measurement Measuring instruments Probabilities Stochastic processes Computer science—Mathematics Mathematical statistics Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Measurement Science and Instrumentation Probability Theory Stochastic Processes Probability and Statistics in Computer Science Estadística matemàtica Probabilitats Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 88-470-4010-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Nota di contenuto | 1 La probabilità -- 2 Rappresentazione dei fenomeni aleatori -- 3 Calcolo elementare delle probabilità -- 4 Calcolo delle probabilità per più variabili -- 5 Funzioni di variabili aleatorie -- 6 Statistica di base: stime -- 7 Statistica di base: verifica di ipotesi -- 8 Il metodo Monte Carlo -- 9 Applicazioni del metodo Monte Carlo -- 10 Inferenza statistica e verosimiglianza -- 11 Minimi quadrati -- 12 Analisi dei dati sperimentali. |
| Record Nr. | UNISA-996466396503316 |
|
Rotondi Alberto
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||
| Milano : , : Springer Milan : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Probabilità, Statistica e Simulazione : Programmi applicativi scritti in R / / by Alberto Rotondi, Paolo Pedroni, Antonio Pievatolo
| Probabilità, Statistica e Simulazione : Programmi applicativi scritti in R / / by Alberto Rotondi, Paolo Pedroni, Antonio Pievatolo |
| Autore | Rotondi Alberto |
| Edizione | [4th ed. 2021.] |
| Pubbl/distr/stampa | Milano : , : Springer Milan : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XV, 621 pagg. 130 figg., 12 figg. a colori.) |
| Disciplina | 519 |
| Collana | La Matematica per il 3+2 |
| Soggetto topico |
Statistics
Measurement Measuring instruments Probabilities Stochastic processes Computer science—Mathematics Mathematical statistics Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences Measurement Science and Instrumentation Probability Theory Stochastic Processes Probability and Statistics in Computer Science Estadística matemàtica Probabilitats Mètodes de simulació |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 88-470-4010-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Nota di contenuto | 1 La probabilità -- 2 Rappresentazione dei fenomeni aleatori -- 3 Calcolo elementare delle probabilità -- 4 Calcolo delle probabilità per più variabili -- 5 Funzioni di variabili aleatorie -- 6 Statistica di base: stime -- 7 Statistica di base: verifica di ipotesi -- 8 Il metodo Monte Carlo -- 9 Applicazioni del metodo Monte Carlo -- 10 Inferenza statistica e verosimiglianza -- 11 Minimi quadrati -- 12 Analisi dei dati sperimentali. |
| Record Nr. | UNINA-9910495347803321 |
|
Rotondi Alberto
|
||
| Milano : , : Springer Milan : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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