Scalable Bayesian Inference For Stochastic Epidemic Processes
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Scalable Bayesian Inference For Stochastic Epidemic Processes
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Author : Martin Burke
language : en
Publisher:
Release Date : 2021
Scalable Bayesian Inference For Stochastic Epidemic Processes written by Martin Burke and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Bayesian Inference For Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods
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Author : Nikolaos Demiris
language : en
Publisher:
Release Date : 2004
Bayesian Inference For Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods written by Nikolaos Demiris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Patterns Of Scalable Bayesian Inference
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Author : Elaine Angelino
language : en
Publisher:
Release Date : 2016
Patterns Of Scalable Bayesian Inference written by Elaine Angelino and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Bayesian statistical decision theory categories.
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with a wide range of assumptions and applicability. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward.
Bayesian Inference For Stochastic Epidemic Models
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Author : Philip Robert Giles
language : en
Publisher:
Release Date : 2005
Bayesian Inference For Stochastic Epidemic Models written by Philip Robert Giles and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Bayesian Inference For Indirectly Observed Stochastic Processes
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Author : Joseph Dureau
language : en
Publisher:
Release Date : 2013
Bayesian Inference For Indirectly Observed Stochastic Processes written by Joseph Dureau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Stochastic processes are mathematical objects that offer a probabilistic representation of how some quantities evolve in time. In this thesis we focus on estimating the trajectory and parameters of dynamical systems in cases where only indirect observations of the driving stochastic process are available. We have first explored means to use weekly recorded numbers of cases of Influenza to capture how the frequency and nature of contacts made with infected individuals evolved in time. The latter was modelled with diffusions and can be used to quantify the impact of varying drivers of epidemics as holidays, climate, or prevention interventions. Following this idea, we have estimated how the frequency of condom use has evolved during the intervention of the Gates Foundation against HIV in India. In this setting, the available estimates of the proportion of individuals infected with HIV were not only indirect but also very scarce observations, leading to specific difficulties. At last, we developed a methodology for fractional Brownian motions (fBM), here a fractional stochastic volatility model, indirectly observed through market prices. The intractability of the likelihood function, requiring augmentation of the parameter space with the diffusion path, is ubiquitous in this thesis. We aimed for inference methods robust to refinements in time discretisations, made necessary to enforce accuracy of Euler schemes. The particle Marginal Metropolis Hastings (PMMH) algorithm exhibits this mesh free property. We propose the use of fast approximate filters as a pre-exploration tool to estimate the shape of the target density, for a quicker and more robust adaptation phase of the asymptotically exact algorithm. The fBM problem could not be treated with the PMMH, which required an alternative methodology based on reparameterisation and advanced Hamiltonian Monte Carlo techniques on the diffusion pathspace, that would also be applicable in the Markovian setting.
Stochastic Epidemic Models With Inference
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Author : Tom Britton
language : en
Publisher: Springer Nature
Release Date : 2019-11-30
Stochastic Epidemic Models With Inference written by Tom Britton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Mathematics categories.
Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.
Bayesian Inference For Stochastic Processes
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Author : LYLE D. BROEMELING
language : en
Publisher: CRC Press
Release Date : 2020-06-30
Bayesian Inference For Stochastic Processes written by LYLE D. BROEMELING and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-30 with Bayesian statistical decision theory categories.
The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from biology, economics, and astronomy reinforce the basic concepts of the subject. R a
Topics In Bayesian Inference And Model Assessment For Partially Observed Stochastic Epidemic Models
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Author : Georgios Aristotelous
language : en
Publisher:
Release Date : 2020
Topics In Bayesian Inference And Model Assessment For Partially Observed Stochastic Epidemic Models written by Georgios Aristotelous and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Stochastic Epidemic Models And Their Statistical Analysis
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Author : Hakan Andersson
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Stochastic Epidemic Models And Their Statistical Analysis written by Hakan Andersson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. Our aim is to present ideas for such models, and methods for their analysis; along the way we make practical use of several probabilistic and statistical techniques. This will be done without focusing on any specific disease, and instead rigorously analyzing rather simple models. The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. The lecture notes require an early graduate level knowledge of probability and They introduce several techniques which might be new to students, but our statistics. intention is to present these keeping the technical level at a minlmum. Techniques that are explained and applied in the lecture notes are, for example: coupling, diffusion approximation, random graphs, likelihood theory for counting processes, martingales, the EM-algorithm and MCMC methods. The aim is to introduce and apply these techniques, thus hopefully motivating their further theoretical treatment. A few sections, mainly in Chapter 5, assume some knowledge of weak convergence; we hope that readers not familiar with this theory can understand the these parts at a heuristic level. The text is divided into two distinct but related parts: modelling and estimation.
Bayesian Inference For Stochastic Processes
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Author : Sean Malory
language : en
Publisher:
Release Date : 2021
Bayesian Inference For Stochastic Processes written by Sean Malory and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.