scholarly journals Markov chain Monte Carlo for the Bayesian analysis of evolutionary trees from aligned molecular sequences

1999 ◽  
pp. 143-162 ◽  
Author(s):  
Michael A. Newton ◽  
Bob Mau ◽  
Bret Larget
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Evolutionary Trees ◽  
Molecular Sequences

Markov Chain Monte Carlo

2015 ◽  
Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Posterior Distribution ◽  
Mcmc Algorithm ◽  
Bayesian Analyses ◽  
Seminal Paper ◽  
Formal Treatment ◽  
High Level

This chapter explains how to implement Bayesian analyses using the Markov chain Monte Carlo (MCMC) algorithm, a set of methods for Bayesian analysis made popular by the seminal paper of Gelfand and Smith (1990). It begins with an explanation of MCMC with a heuristic, high-level treatment of the algorithm, describing its operation in simple terms with a minimum of formalism. In this first part, the chapter explains the algorithm so that all readers can gain an intuitive understanding of how to find the posterior distribution by sampling from it. Next, the chapter offers a somewhat more formal treatment of how MCMC is implemented mathematically. Finally, this chapter discusses implementation of Bayesian models via two routes—by using software and by writing one's own algorithm.

scholarly journals Solar Bayesian Analysis Toolkit—A New Markov Chain Monte Carlo IDL Code for Bayesian Parameter Inference

2021 ◽  
Vol 252 (1) ◽  
pp. 11
Author(s):  
Sergey A. Anfinogentov ◽  
Valery M. Nakariakov ◽  
David J. Pascoe ◽  
Christopher R. Goddard
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Parameter Inference

scholarly journals Improving Bayesian analysis for LISA Pathfinder using an efficient Markov Chain Monte Carlo method

2014 ◽  
Vol 37 (1) ◽  
pp. 109-125 ◽  
Author(s):  
Luigi Ferraioli ◽  
Edward K. Porter ◽  
Michele Armano ◽  
Heather Audley ◽  
Giuseppe Congedo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Bayesian Analysis ◽  
Lisa Pathfinder

Issues in Bayesian Analysis of Neural Network Models

1998 ◽  
Vol 10 (3) ◽  
pp. 749-770 ◽  
Author(s):  
Peter Müller ◽  
David Rios Insua
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Neural Network Models

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed-architecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data-driven procedure to identify sensible architectures.

scholarly journals Bayesian analysis of spectral mixture data using Markov Chain Monte Carlo Methods

2006 ◽  
Vol 81 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Saïd Moussaoui ◽  
Cédric Carteret ◽  
David Brie ◽  
Ali Mohammad-Djafari
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Monte Carlo Methods ◽  
Spectral Mixture
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