MCMC-ODPR: Primer design optimization using Markov Chain Monte Carlo sampling

Markov Chain Monte Carlo Sampling using Direct Search Optimization

Markov Chain Monte Carlo and Gibbs Sampling

A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...

Markov Chain Monte Carlo and Gibbs Sampling

A major limitation towards more widespread implementation of Bayesian approaches is that obtaining the posterior distribution often requires the integration of high-dimensional functions. This can be computationally very difficult, but several approaches short of direct integration have been proposed (reviewed by Smith 1991, Evans and Swartz 1995, Tanner 1996). We focus here on Markov Chain Mon...

Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling

Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery et al. Mon Weather Rev 133:1155–1174, 2005) has recommended the Expec...

Representative Subgraph Sampling using Markov Chain Monte Carlo Methods

Bioinformatics and the Internet keep generating graph data with thousands of nodes. Most traditional graph algorithms for data analysis are too slow for analysing these large graphs. One way to work around this problem is to sample a smaller ‘representative subgraph’ from the original large graph. Existing representative subgraph sampling algorithms either randomly select sets of nodes or edges...