{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":28,"n_cols":5,"rows_kept":28,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[13,2,13,3],[15,2,15,3],[16,2,16,3],[20,2,20,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910825559603321"]
[null,null,"Autore",null,"Candy James V."]
[null,null,"Titolo",null,"Bayesian signal processing : classical, modern, and particle filtering methods / / James V. Candy"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : John Wiley & Sons Inc., , [2016]"]
[{"$nulls":4},"[Piscataqay, New Jersey] : , : IEEE Xplore, , [2016]"]
[null,null,"ISBN",null,"1-119-12548-0"]
[{"$nulls":4},"1-5231-1488-6"]
[{"$nulls":4},"1-119-12547-2"]
[{"$nulls":4},"1-119-12549-9"]
[null,null,"Edizione",null,"[Second edition.]"]
[null,null,"Descrizione fisica",null,"1 online resource (637 p.)"]
[null,null,"Collana",null,"Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control ; ; 54"]
[null,null,"Disciplina",null,"621.382/201519542"]
[null,null,"Soggetti",null,"Signal processing - Mathematics"]
[{"$nulls":4},"Bayesian statistical decision theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references at the end of each chapters and index."]
[null,null,"Nota di contenuto",null,"Bayesian Signal Processing -- Contents -- Preface to Second Edition -- References -- Preface to First Edition -- References -- Acknowledgments -- List of Abbreviations -- 1 Introduction -- 1.1 Introduction -- 1.2 Bayesian Signal Processing -- 1.3 Simulation-Based Approach to Bayesian Processing -- 1.3.1 Bayesian Particle Filter -- 1.4 Bayesian Model-Based Signal Processing -- 1.5 Notation and Terminology -- References -- 2 Bayesian Estimation -- 2.1 Introduction -- 2.2 Batch Bayesian Estimation -- 2.3 Batch Maximum Likelihood Estimation -- 2.3.1 Expectation-Maximization Approach to Maximum Likelihood -- 2.3.2 EM for Exponential Family of Distributions -- 2.4 Batch Minimum Variance Estimation -- 2.5 Sequential Bayesian Estimation -- 2.5.1 Joint Posterior Estimation -- 2.5.2 Filtering Posterior Estimation -- 2.5.3 Likelihood Estimation -- 2.6 Summary -- References -- 3 Simulation-Based Bayesian Methods -- 3.1 Introduction -- 3.2 Probability Density Function Estimation -- 3.3 Sampling Theory -- 3.3.1 Uniform Sampling Method -- 3.3.2 Rejection Sampling Method -- 3.4 Monte Carlo Approach -- 3.4.1 Markov Chains -- 3.4.2 Metropolis-Hastings Sampling -- 3.4.3 Random Walk Metropolis-Hastings Sampling -- 3.4.4 Gibbs Sampling "]
[{"$nulls":4},"-- 3.4.5 Slice Sampling -- 3.5 Importance Sampling -- 3.6 Sequential Importance Sampling -- 3.7 Summary -- References -- 4 State-Space Models for Bayesian Processing -- 4.1 Introduction -- 4.2 Continuous-Time State-Space Models -- 4.3 Sampled-Data State-Space Models -- 4.4 Discrete-Time State-Space Models -- 4.4.1 Discrete Systems Theory -- 4.5 Gauss-Markov State-Space Models -- 4.5.1 Continuous-Time/Sampled-Data Gauss-Markov Models -- 4.5.2 Discrete-Time Gauss-Markov Models -- 4.6 Innovations Model -- 4.7 State-Space Model Structures -- 4.7.1 Time Series Models -- 4.7.2 State-Space and Time Series Equivalence Models."]
[{"$nulls":4},"4.8 Nonlinear (Approximate) Gauss-Markov State-Space Models -- 4.9 Summary -- References -- 5 Classical Bayesian State-Space Processors -- 5.1 Introduction -- 5.2 Bayesian Approach to the State-Space -- 5.3 Linear Bayesian Processor (Linear Kalman Filter) -- 5.4 Linearized Bayesian Processor (Linearized Kalman Filter) -- 5.5 Extended Bayesian Processor (Extended Kalman Filter) -- 5.6 Iterated-Extended Bayesian Processor (Iterated-Extended Kalman Filter) -- 5.7 Practical Aspects of Classical Bayesian Processors -- 5.8 Case Study: RLC Circuit Problem -- 5.9 Summary -- References -- 6 Modern Bayesian State-Space Processors -- 6.1 Introduction -- 6.2 Sigma-Point (Unscented) Transformations -- 6.2.1 Statistical Linearization -- 6.2.2 Sigma-Point Approach -- 6.2.3 SPT for Gaussian Prior Distributions -- 6.3 Sigma-Point Bayesian Processor (Unscented Kalman Filter) -- 6.3.1 Extensions of the Sigma-Point Processor -- 6.4 Quadrature Bayesian Processors -- 6.5 Gaussian Sum (Mixture) Bayesian Processors -- 6.6 Case Study: 2D-Tracking Problem -- 6.7 Ensemble Bayesian Processors (Ensemble Kalman Filter) -- 6.8 Summary -- References -- 7 Particle-Based Bayesian State-Space Processors -- 7.1 Introduction -- 7.2 Bayesian State-Space Particle Filters -- 7.3 Importance Proposal Distributions -- 7.3.1 Minimum Variance Importance Distribution -- 7.3.2 Transition Prior Importance Distribution -- 7.4 Resampling -- 7.4.1 Multinomial Resampling -- 7.4.2 Systematic Resampling -- 7.4.3 Residual Resampling -- 7.5 State-Space Particle Filtering Techniques -- 7.5.1 Bootstrap Particle Filter -- 7.5.2 Auxiliary Particle Filter -- 7.5.3 Regularized Particle Filter -- 7.5.4 MCMC Particle Filter -- 7.5.5 Linearized Particle Filter -- 7.6 Practical Aspects of Particle Filter Design -- 7.6.1 Sanity Testing -- 7.6.2 Ensemble Estimation -- 7.6.3 Posterior Probability Validation."]
[{"$nulls":4},"7.6.4 Model Validation Testing -- 7.7 Case Study: Population Growth Problem -- 7.8 Summary -- References -- 8 Joint Bayesian State/Parametric Processors -- 8.1 Introduction -- 8.2 Bayesian Approach to Joint State/Parameter Estimation -- 8.3 Classical/Modern Joint Bayesian State/Parametric Processors -- 8.3.1 Classical Joint Bayesian Processor -- 8.3.2 Modern Joint Bayesian Processor -- 8.4 Particle-Based Joint Bayesian State/Parametric Processors -- 8.4.1 Parametric Models -- 8.4.2 Joint Bayesian State/Parameter Estimation -- 8.5 Case Study: Random Target Tracking Using a Synthetic Aperture Towed Array -- 8.6 Summary -- References -- 9 Discrete Hidden Markov Model Bayesian Processors -- 9.1 Introduction -- 9.2 Hidden Markov Models -- 9.2.1 Discrete-Time Markov Chains -- 9.2.2 Hidden Markov Chains -- 9.3 Properties of the Hidden Markov Model -- 9.4 HMM Observation Probability: Evaluation Problem -- 9.5 State Estimation in HMM: The Viterbi Technique -- 9.5.1 Individual Hidden State Estimation -- 9.5.2 Entire Hidden State Sequence Estimation -- 9.6 Parameter Estimation in HMM: The EM/Baum-Welch Technique -- 9.6.1 Parameter Estimation with State Sequence Known -- 9.6.2 Parameter Estimation with State Sequence Unknown -- 9.7 Case Study: Time-Reversal Decoding -- 9.8 Summary -- References -- 10 "]
[{"$nulls":4},"Sequential Bayesian Detection -- 10.1 Introduction -- 10.2 Binary Detection Problem -- 10.2.1 Classical Detection -- 10.2.2 Bayesian Detection -- 10.2.3 Composite Binary Detection -- 10.3 Decision Criteria -- 10.3.1 Probability-of-Error Criterion -- 10.3.2 Bayes Risk Criterion -- 10.3.3 Neyman-Pearson Criterion -- 10.3.4 Multiple (Batch) Measurements -- 10.3.5 Multichannel Measurements -- 10.3.6 Multiple Hypotheses -- 10.4 Performance Metrics -- 10.4.1 Receiver Operating Characteristic (ROC) Curves -- 10.5 Sequential Detection -- 10.5.1 Sequential Decision Theory."]
[{"$nulls":4},"10.6 Model-Based Sequential Detection -- 10.6.1 Linear Gaussian Model-Based Processor -- 10.6.2 Nonlinear Gaussian Model-Based Processor -- 10.6.3 Non-Gaussian Model-Based Processor -- 10.7 Model-Based Change (Anomaly) Detection -- 10.7.1 Model-Based Detection -- 10.7.2 Optimal Innovations Detection -- 10.7.3 Practical Model-Based Change Detection -- 10.8 Case Study: Reentry Vehicle Change Detection -- 10.8.1 Simulation Results -- 10.9 Summary -- References -- 11 Bayesian Processors for Physics-Based Applications -- 11.1 Optimal Position Estimation for the Automatic Alignment -- 11.1.1 Background -- 11.1.2 Stochastic Modeling of Position Measurements -- 11.1.3 Bayesian Position Estimation and Detection -- 11.1.4 Application: Beam Line Data -- 11.1.5 Results: Beam Line (KDP Deviation) Data -- 11.1.6 Results: Anomaly Detection -- 11.2 Sequential Detection of Broadband Ocean Acoustic Sources -- 11.2.1 Background -- 11.2.2 Broadband State-Space Ocean Acoustic Propagators -- 11.2.3 Discrete Normal-Mode State-Space Representation -- 11.2.4 Broadband Bayesian Processor -- 11.2.5 Broadband Particle Filters -- 11.2.6 Broadband Bootstrap Particle Filter -- 11.2.7 Bayesian Performance Metrics -- 11.2.8 Sequential Detection -- 11.2.9 Broadband BSP Design -- 11.2.10 Summary -- 11.3 Bayesian Processing for Biothreats -- 11.3.1 Background -- 11.3.2 Parameter Estimation -- 11.3.3 Bayesian Processor Design -- 11.3.4 Results -- 11.4 Bayesian Processing for the Detection of Radioactive Sources -- 11.4.1 Physics-Based Processing Model -- 11.4.2 Radionuclide Detection -- 11.4.3 Implementation -- 11.4.4 Detection -- 11.4.5 Data -- 11.4.6 Radionuclide Detection -- 11.4.7 Summary -- 11.5 Sequential Threat Detection: An X-ray Physics-Based Approach -- 11.5.1 Physics-Based Models -- 11.5.2 X-ray State-Space Simulation -- 11.5.3 Sequential Threat Detection -- 11.5.4 Summary."]
[{"$nulls":4},"11.6 Adaptive Processing for Shallow Ocean Applications -- 11.6.1 State-Space Propagator -- 11.6.2 Processors -- 11.6.3 Model-Based Ocean Acoustic Processing -- 11.6.4 Summary -- References -- Appendix: Probability and Statistics Overview -- A.1 Probability Theory -- A.2 Gaussian Random Vectors -- A.3 Uncorrelated Transformation: Gaussian Random Vectors -- References -- Index -- Wiley Series on Adaptive and Cognitive Dynamic Systems -- EULA."]
[null,null,null,"Sommario/riassunto","Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the 'Bayesian approach' to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as well as classical techniques."]