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Biblioteca

MARC Lista (tabellare)
Applying Quantitative Bias Analysis to Epidemiologic Data / Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash
Applying Quantitative Bias Analysis to Epidemiologic Data / Matthew P. Fox, Richard F. MacLehose, Timothy L. Lash
Autore Fox, Matthew P.
Edizione [2. rev. ed]
Pubbl/distr/stampa Cham, : Springer, 2021
Descrizione fisica xvi, 467 p. : ill. ; 24 cm
Altri autori (Persone) Lash, Timothy L.
MacLehose, Richard F.
Soggetto topico 62-XX - Statistics [MSC 2020]
62H20 - Measures of association (correlation, canonical correlation, etc.) [MSC 2020]
62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020]
92D30 - Epidemiology [MSC 2020]
Soggetto non controllato Bayesian Methods
Bias Analysis
Classification
Confounding
Epidemiology
Information bias
Master Patient Index
Measurement bias
Measurement error
Misclassification
Monte Carlo analysis
Selection bias
Sensitivity Analysis
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00274564
Fox, Matthew P.  
Cham, : Springer, 2021
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Bayes Theory / J. A. Hartigan
Bayes Theory / J. A. Hartigan
Autore Hartigan, John A.
Pubbl/distr/stampa New York, : Springer-Verlag, 1983
Descrizione fisica xii, 146 p. : ill. ; 24 cm
Soggetto topico 62-XX - Statistics [MSC 2020]
62A01 - Foundations and philosophical topics in statistics [MSC 2020]
62C10 - Bayesian problems; characterization of Bayes procedures [MSC 2020]
Soggetto non controllato Bayesian Methods
Boundary Element Methods
Conditional probability
Finite
Innovation
Logic
Objects
Probability
Probability axioms
Probability distributions
Proofs
Shapes
Similarity
Techniques
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto This book is based on lectures given at Yale in 1971-1981 to students prepared with a course in measure-theoretic probability. It contains one technical innovation-probability distributions in which the total probability is infinite. Such improper distributions arise embarras­ singly frequently in Bayes theory, especially in establishing correspondences between Bayesian and Fisherian techniques. Infinite probabilities create interesting complications in defining conditional probability and limit concepts. The main results are theoretical, probabilistic conclusions derived from probabilistic assumptions. A useful theory requires rules for constructing and interpreting probabilities. Probabilities are computed from similarities, using a formalization of the idea that the future will probably be like the past. Probabilities are objectively derived from similarities, but similarities are sUbjective judgments of individuals. Of course the theorems remain true in any interpretation of probability that satisfies the formal axioms. My colleague David Potlard helped a lot, especially with Chapter 13. Dan Barry read proof. vii Contents CHAPTER 1 Theories of Probability 1. 0. Introduction 1 1. 1. Logical Theories: Laplace 1 1. 2. Logical Theories: Keynes and Jeffreys 2 1. 3. Empirical Theories: Von Mises 3 1. 4. Empirical Theories: Kolmogorov 5 1. 5. Empirical Theories: Falsifiable Models 5 1. 6. Subjective Theories: De Finetti 6 7 1. 7. Subjective Theories: Good 8 1. 8. All the Probabilities 10 1. 9. Infinite Axioms 11 1. 10. Probability and Similarity 1. 11. References 13 CHAPTER 2 Axioms 14 2. 0. Notation 14 2. 1. Probability Axioms 14 2. 2.
Record Nr. UNICAMPANIA-VAN00268567
Hartigan, John A.  
New York, : Springer-Verlag, 1983
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Bayesian Compendium / Marcel van Oijen
Bayesian Compendium / Marcel van Oijen
Autore Oijen, Marcel van
Pubbl/distr/stampa Cham, : Springer, 2020
Descrizione fisica xiv, 204 p. : ill. ; 24 cm
Soggetto topico 62-XX - Statistics [MSC 2020]
62A01 - Foundations and philosophical topics in statistics [MSC 2020]
62F15 - Bayesian inference [MSC 2020]
62M20 - Inference from stochastic processes and prediction; filtering [MSC 2020]
62R07 - Statistical aspects of big data and data science [MSC 2020]
Soggetto non controllato Bayesian Methods
Data Assimilation
Goodness-of-fit test
Graphical modelling
Linear modelling
MSE-decomposition
Multidimensionality
Risk analysis
Sampling from the posterior
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00248744
Oijen, Marcel van  
Cham, : Springer, 2020
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Chemical Master Equation for Large Biological Networks : State-space Expansion Methods Using AI / Don Kulasiri, Rahul Kosarwal
Chemical Master Equation for Large Biological Networks : State-space Expansion Methods Using AI / Don Kulasiri, Rahul Kosarwal
Autore Kulasiri, Don
Pubbl/distr/stampa Singapore, : Springer, 2021
Descrizione fisica xviii, 217 p. : ill. ; 24 cm
Altri autori (Persone) Kosarwal, Rahul
Soggetto topico 60J28 - Applications of continuous-time Markov processes on discrete state spaces [MSC 2020]
68T07 - Artificial neural networks and deep learning [MSC 2020]
92-XX - Biology and other natural sciences [MSC 2020]
92C40 - Biochemistry, molecular biology [MSC 2020]
92C42 - Systems biology, networks [MSC 2020]
92C45 - Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) [MSC 2020]
Soggetto non controllato Artificial Intelligence
Bayesian Methods
Biochemical Networks
Bionetworks
Markov Processes
Markov graphs
Markov tree
Model Building
Modeling and integration
Numerical simulations
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00283061
Kulasiri, Don  
Singapore, : Springer, 2021
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Discrete tomography : foundations, algorithms, and applications / Gabor T. Herman, Attila Kuba editors
Discrete tomography : foundations, algorithms, and applications / Gabor T. Herman, Attila Kuba editors
Pubbl/distr/stampa Boston [etc.], : Birkhäuser, 1999
Descrizione fisica XXII, 479 p. : ill. ; 24 cm
Soggetto topico 68-XX - Computer science [MSC 2020]
92-XX - Biology and other natural sciences [MSC 2020]
Soggetto non controllato 3-D Torus
Bayesian Methods
Binary Tomography
Computer imaging
Diffuse planar tomography
Discrete images
Electron Microscopy
Image Processing
Image projections
Multidimensional image processing
Multidimensional images
Nonbinary iterative algorithms
Orthogonal projections
Radiographic data
Symbolic projections
ISBN 08-17-64101-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00044478
Boston [etc.], : Birkhäuser, 1999
Materiale a stampa
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Discrete tomography : foundations, algorithms, and applications / Gabor T. Herman, Attila Kuba editors
Discrete tomography : foundations, algorithms, and applications / Gabor T. Herman, Attila Kuba editors
Pubbl/distr/stampa New York, : Springer ; Boston, : Birkhäuser, 1999
Descrizione fisica xxii, 479 p. : ill. ; 24 cm
Soggetto topico 68-XX - Computer science [MSC 2020]
92-XX - Biology and other natural sciences [MSC 2020]
Soggetto non controllato 3-D Torus
Bayesian Methods
Binary Tomography
Computer imaging
Diffuse planar tomography
Discrete images
Electron Microscopy
Image Processing
Image projections
Multidimensional image processing
Multidimensional images
Nonbinary iterative algorithms
Orthogonal projections
Radiographic data
Symbolic projections
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00299627
New York, : Springer ; Boston, : Birkhäuser, 1999
Materiale a stampa
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Extended abstracts fall 2015 : Biomedical Big Data / Guadalupe Gómez, Pere Puig, M.Luz Calle Editors ; Statistics for Low Dose Radiation Research / Elizabeth A. Ainsbury ... [et al.] editors
Extended abstracts fall 2015 : Biomedical Big Data / Guadalupe Gómez, Pere Puig, M.Luz Calle Editors ; Statistics for Low Dose Radiation Research / Elizabeth A. Ainsbury ... [et al.] editors
Pubbl/distr/stampa Cham, : Birkhäuser, 2017
Descrizione fisica vii, 131 p. : ill. ; 24 cm
Soggetto topico 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020]
62N01 - Censored data models [MSC 2020]
62P10 - Applications of statistics to biology and medical sciences; meta analysis [MSC 2020]
92B15 - General Biostatistics [MSC 2020]
92C60 - Medical epidemiology [MSC 2020]
92D30 - Epidemiology [MSC 2020]
Soggetto non controllato Bayesian Methods
Epidemiology
Genetics
HIV research
High-Dimensional Data
Integrative omics
Inverse regression
Ionising radiation
Low dose
Penalized regression
Radiation Biology
Survival analysis
Time series
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00123864
Cham, : Birkhäuser, 2017
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Learning in the Absence of Training Data / Dalia Chakrabarty
Learning in the Absence of Training Data / Dalia Chakrabarty
Autore Chakrabarty, Dalia
Pubbl/distr/stampa Cham, : Springer, 2023
Descrizione fisica xviii, 227 p. : ill. ; 24 cm
Soggetto topico 60-XX - Probability theory and stochastic processes [MSC 2020]
62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020]
Soggetto non controllato Bayesian Methods
Choosing Priors on Unknown Parameters
Generating the originally-absent training data
Prediction given Test Data
Supervised learning
Training Data
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00278850
Chakrabarty, Dalia  
Cham, : Springer, 2023
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Oceanographic Analysis with R / Dan E. Kelley
Oceanographic Analysis with R / Dan E. Kelley
Autore Kelley, Dan E.
Pubbl/distr/stampa New York, : Springer, 2018
Descrizione fisica xxi, 290 p. : ill. ; 24 cm
Soggetto topico 62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020]
86A05 - Hydrology, hydrography, oceanography [MSC 2020]
Soggetto non controllato Bayesian Methods
Data Analysis
Ecosystem modeling
Graphics
Instrumentation
Oceanography
Time Series Analysis
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Titolo uniforme
Record Nr. UNICAMPANIA-VAN00125103
Kelley, Dan E.  
New York, : Springer, 2018
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Probabilistic Risk Analysis and Bayesian Decision Theory / Marcel van Oijen, Mark Brewer
Probabilistic Risk Analysis and Bayesian Decision Theory / Marcel van Oijen, Mark Brewer
Autore Oijen, Marcel van
Pubbl/distr/stampa Cham, : Springer, 2022
Descrizione fisica xiii, 114 p. : ill. ; 24 cm
Altri autori (Persone) Brewer, Mark
Soggetto topico 60-XX - Probability theory and stochastic processes [MSC 2020]
62-XX - Statistics [MSC 2020]
91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020]
Soggetto non controllato Bayesian Methods
Decision theory
Hazards
Probability theory
Risk analysis
System Vulnerability
Uncertainty Quantification
Utility
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNICAMPANIA-VAN00278008
Oijen, Marcel van  
Cham, : Springer, 2022
Materiale a stampa
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