We present a method for controlling the output of procedural modeling programs using Sequential Monte Carlo (SMC). Previous probabilistic methods for controlling procedural models use Markov Chain Monte Carlo (MCMC), which receives control feedback only for completely-generated models. In contrast, SMC receives feedback incrementally on incomplete models, allowing it to reallocate computational...

Realistic statistical models often give rise to probability distributions that are computationally difficult to use for inference. Fortunately, we now have a collection of algorithms, known as Markov chain Monte Carlo (MCMC), that has brought many of these models within our computational reach. In turn, this has lead to a staggering amount of both theoretical and applied work on MCMC. Thus we d...

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on Jeffreys’ prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated...

We prove an upper bound on the convergence rate of Markov Chain Monte Carlo (MCMC) algorithms for the important special case when the state space can be aggregated into a smaller space, such that the aggregated chain approximately preserves the Markov property.

− In hierarchical learning machines such as neural networks, Bayesian learning provides better generalization performance than maximum likelihood estimation. However, its accurate approximation using the Markov chain Monte Carlo (MCMC) method requires a huge computational cost. The exchange Monte Carlo (EMC) method was proposed as an improvement on the MCMC method. Although it has been shown to...

The goal of this Random Walks project is to code and experiment the Markov Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this report, we present the plots of cost function H by varying the parameters like q (Number of colors that can be used in coloring) and c (Average node degree). The results are obtained by using simulated annealing scheme, where the temperature (inve...

The goal of a Markov Chain Monte Carlo (MCMC) simulation is to generate samples from a target probability distribution π by simulating a Markov chain whose stationary distribution is π. However, often this ideal is not achieved, and the practitioner actually samples from an approximate distribution π̃ that is close to π in variation distance. These circumstances have spawned an array of literatu...

We describe the importance and widespread use of Markov chain Monte Carlo (MCMC) algorithms, with an emphasis on the roles in which theoretical analysis can help with their practical implementation. In particular, we discuss how to achieve rigorous quantitative bounds on convergence to stationarity using the coupling method together with drift and minorisation conditions. We also discuss recent...

In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic. More specifically, show can be used to estimate the absolute value of free energy, which in contrast existing MCMC-based limited only energy differences. We effectiveness proposed method t...

We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learni...