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Convergence of a Particle-Based Approximation of the Block Online Expectation Maximization Algorithm
Online variants of the Expectation Maximization (EM) algorithm have recently been proposed to perform parameter inference with large data sets or data streams, in independent latent models and in hidden Markov models. Nevertheless, the convergence ...
Efficient MCMC for Binomial Logit Models
This article deals with binomial logit models where the parameters are estimated within a Bayesian framework. Such models arise, for instance, when repeated measurements are available for identical covariate patterns. To perform MCMC sampling, we ...
Bayesian Learning of Noisy Markov Decision Processes
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov ...
Adaptive Equi-Energy Sampler: Convergence and Illustration
Markov chain Monte Carlo (MCMC) methods allow to sample a distribution known up to a multiplicative constant. Classical MCMC samplers are known to have very poor mixing properties when sampling multimodal distributions. The Equi-Energy sampler is an ...
Posterior Expectation of Regularly Paved Random Histograms
We present a novel method for averaging a sequence of histogram states visited by a Metropolis-Hastings Markov chain whose stationary distribution is the posterior distribution over a dense space of tree-based histograms. The computational efficiency of ...
Small Variance Estimators for Rare Event Probabilities
Improving Importance Sampling estimators for rare event probabilities requires sharp approximations of conditional densities. This is achieved for events defined through large exceedances of the empirical mean of summands of a random walk, in the domain ...
Particle Algorithms for Optimization on Binary Spaces
We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling ...
Self-Avoiding Random Dynamics on Integer Complex Systems
This article introduces a new specialized algorithm for equilibrium Monte Carlo sampling of binary-valued systems, which allows for large moves in the state space. This is achieved by constructing self-avoiding walks (SAWs) in the state space. As a ...
Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model
Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health ...