4 papers
Variational Autoencoder for Generating Broader-Spectrum prior Proposals in Markov chain Monte Carlo Methods
Marcio Borges, Felipe Pereira, Michel Tosin
This study uses a Variational Autoencoder method to enhance the efficiency and applicability of Markov Chain Monte Carlo (McMC) methods by generating broader-spectrum prior proposa…
A Framework to Analyze Multiscale Sampling MCMC Methods
Lucas Seiffert, Felipe Pereira
We consider the theoretical analysis of Multiscale Sampling Methods, which are a new class of gradient-free Markov chain Monte Carlo (MCMC) methods for high dimensional inverse dif…
Estimating the Effective Sample Size for an inverse problem in subsurface flows
Lucas Seiffert, Felipe Pereira
The Effective Sample Size (ESS) and Integrated Autocorrelation Time (IACT) are two popular criteria for comparing Markov Chain Monte Carlo (MCMC) algorithms and detecting their con…
Dimension of Gibbs measures with infinite entropy
Felipe Pérez Pereira
We study the Hausdorff dimension of Gibbs measures with infinite entropy with respect to maps of the interval with countably many branches. We show that under simple conditions, su…