Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Dani Gamerman, Hedibert F. Lopes

Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference


Markov.Chain.Monte.Carlo.Stochastic.Simulation.for.Bayesian.Inference.pdf
ISBN: 9781584885870 | 344 pages | 9 Mb


Download Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference



Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference Dani Gamerman, Hedibert F. Lopes
Publisher: Taylor & Francis



Jan 9, 2014 - This article explains this nonparametric Bayesian inference, shows how Mathematica's capacity for memoization supports probabilistic programming features, and demonstrates this capability through two examples, learning systems of relations and learning arithmetic functions based . As a case study, we consider a stochastic model of the Hes1 system expressed in terms of stochastic differential equations (SDEs) to which rigorous likelihood methods of inference can be applied. Mar 21, 2013 - I recently read a new paper by Sumio Watanabe on A Widely applicable Bayesian information criterion (WBIC)[1] (and to appear in JMLR soon) that provides a new, theoretically grounded and easy to implement method of approximating the marginal likelihood, which I will briefly describe in this post. The results of the MCMC Finally, we formulated a discrete-time, direct transmission, stochastic model for the spread of dengue virus and used Markov chain Monte Carlo (MCMC) methods to perform Bayesian inference and estimate the basic reproduction number. May 27, 2011 - Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference (Texts in Statistical Science Series). Aug 15, 2008 - In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. Sep 21, 2013 - In contrast, sequential Monte Carlo methods (SMCM) offer a probabilistic framework that is suited to non-linear and non-Gaussian state-space models. Jul 28, 2007 - Motivation: In this study, we address the problem of estimating the parameters of regulatory networks and provide the first application of Markov chain Monte Carlo (MCMC) methods to experimental data. Apr 29, 2013 - As a likelihood-based method, the EM approach deals naturally with the stochastic nature of mutational processes, and enables us to use model selection criteria, such as the Bayesian information criterion (BIC) [18], to decide which number of processes has the strongest statistical support. Meaningful error estimates of the inferred mutational signatures can be derived either analytically or numerically with Markov chain Monte Carlo (MCMC) methods. Let us now explain stochastic memoization and then look at how to implement Metropolis-Hastings querying, which uses memoization to help implement Markov chain Monte Carlo-driven inference. Dec 17, 2013 - Various approaches based on different models have been used to infer the network from observed gene expression data, such as the Markov Chain Monte Carlo (MCMC) methods for the dynamic Bayesian network model [6] and the ordinary differential equation model [7], as well as the Due to the 'stochastic' nature of the gene expression, the Kalman filtering approach based on the state-space model is one of the most competitive methods for inferring the GRN. Samples from the annealed distribution can be generated using MCMC methods like hybrid (Hamiltonian) Monte Carlo or by slice sampling. Apr 7, 2014 - Moreover, the mean trajectory of the stochastic model (4.1) calculated using Monte Carlo simulations involving the mean of the posterior distribution is displayed in Figure 9.





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