Bayesian Inference for Stochastic Epidemic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Stochastic Epidemic Models write by Philip Robert Giles. This book was released on 2005. Bayesian Inference for Stochastic Epidemic Models available in PDF, EPUB and Kindle.
Bayesian Inference for Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods
Bayesian Inference for Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods write by Nikolaos Demiris. This book was released on 2004. Bayesian Inference for Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods available in PDF, EPUB and Kindle.
Stochastic Epidemic Models with Inference
Stochastic Epidemic Models with Inference - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Epidemic Models with Inference write by Tom Britton. This book was released on 2019-11-30. Stochastic Epidemic Models with Inference available in PDF, EPUB and Kindle. 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.
Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models
Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models write by Georgios Aristotelous. This book was released on 2020. Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models available in PDF, EPUB and Kindle.
Bayesian Inference for Stochastic Processes
Bayesian Inference for Stochastic Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Inference for Stochastic Processes write by Lyle D. Broemeling. This book was released on 2017-12-12. Bayesian Inference for Stochastic Processes available in PDF, EPUB and Kindle. This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.
