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

Dec 2, 2012 - We provide a gentle introduction to ABC and some alternative approaches in our recent Ecology Letters review on “statisitical inference for stochastic simulation models”. Feb 2, 2006 - Last time we explained how to build a logistic oil production profile using a Stochastic Bass Model which can be seen as a stochastic equivalent of the logistic curve used by peakoilers. Lopes 2006 | 344 Pages | ISBN: 1584885874 | PDF | 9 MB. Feb 5, 2013 - I was reminded of this idea when reading Christian Robert and George Casella's fun new book, Introducing Monte Carlo Methods with R. Description: Stochastic simulation and MCMC inference of structure from genetic data. Other reconstruction methods as maximum likelihood, bayesian inference or maximum parsimony may equally profit from secondary structure inclusion. The proposal The package also provides some functions for Bayesian inference including Bayesian Credible Intervals (BCI) and Deviance Information Criterion (DIC) calculation. Jul 5, 2008 - In particular I have been interested in MCMC methods related to simulation-based inference, since this enables us to analyze very complicated stochastic systems for large data sets as appearing in modern statistical applications, including spatial statistics. Mol Phylogenet Evol A simulation study comparing the performance of Bayesian Markov Chain Monte Carlo sampling and bootstrapping in assessing phylogenetic confidence. Schöniger M, von Haeseler A: A stochastic model for the evolution of autocorrelated DNA sequences. Description: Performs general Metropolis-Hastings Markov Chain Monte Carlo sampling of a user defined function which returns the un-normalized value (likelihood times prior) of a Bayesian model. Nice bridge between probability and statistics and gives a modern twist to the discussion by introducing computational issues involved in generating samples from specific distributions, including accept-reject methods and basic MCMC methods. Jan 2, 2013 - Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition By Dani Gamerman, Hedibert F. Recently, in connection to Bayesian inference, the problem with unknown normalizing constants of the likelihood term has been solved using an MCMC auxiliary variable method as introduced in Møller et al. I do most of my work in statistical methodology and applied statistics, but sometimes I back up my . This post is an attempt to apply Particle filtering can be seen as a generalization of the Kalman filter and is sometimes encountered under various names such as the bootstrap filter, the condensation method, the Bayesian filter or the sequential Monte-Carlo Markov Chain (MCMC). The EasyABC solution is provided below. The EasyABC package, available from CRAN, To give a demonstration, I implemented the parameter inference of a normal distribution using the ABC-MCMC algorithm proposed by Marjoram that I coded by hand in my previous post on ABC in EasyABC.