An adaptive Monte-Carlo Markov chain algorithm for inference from mixture signals

Adaptive Incremental Mixture Markov chain Monte Carlo

We propose Adaptive Incremental Mixture Markov chain Monte Carlo (AIMM), a novel approach to sample from challenging probability distributions defined on a general state-space. Typically, adaptive MCMC methods recursively update a parametric proposal kernel with a global rule; by contrast AIMM locally adapts a non-parametric kernel. AIMM is based on an independent Metropolis-Hastings proposal d...

Markov Chain Monte Carlo for Statistical Inference

Markov chain Monte Carlo for Bayesian inference

The chord length transform (CLT) is a useful tool to analyze fibre structures. Assuming e.g. arandom process of straight fibres then a realization of such a process can be observed in a binaryimage. The CLT maps to each point in the foreground of a binary image and to each direction thelength of the related chord, where the chord is the connecting part of a line in the direction...

An Adaptive Markov Chain Monte Carlo Method for GARCH Model

We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC method itself. It turns out that autocorrelations between the data generated with our adaptive proposal density are greatly reduced. Thus it is concluded t...

Adaptive Markov Chain Monte Carlo through Regeneration

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