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Variational Inference with Mixture Model Approximation for Applications in Robotics

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Linear Bayesian Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurately estimating the system dynamics, and in particular when we allow for corre ...

2013

Robust and Private Bayesian Inference

Christos Dimitrakakis, Aikaterini Mitrokotsa

We examine the robustness and privacy properties of Bayesian inference under assumptions on the prior, but without any modifications to the Bayesian framework. First, we generalise the concept of differential privacy to arbitrary dataset distances, outcome ...

2013

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and he ...

2011

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

2011

Metropolis-Hastings sampling of paths

Michel Bierlaire, Gunnar Flötteröd

We consider the previously unsolved problem of sampling cycle-free paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all p ...

2011

Compromise Pitman estimators

Nicolas Fournier

In this paper, we investigate the construction of compromise estimators of location and scale, by averaging over several models selected among a specified large set of possible models. The weight given to each distribution is based on the profile likelihoo ...

Elsevier2011

Bayesian detection of asynchronous EEG patterns

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Nicolas Bourdaud

We proposed a Bayesian model for the detection of asynchronous EEG patterns. Based on a skew normal model of the pattern of interest in the time-domain and on the assumption that background activity can be modeled as colored noise, we estimate both the pat ...

2011

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Matthias Seeger

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (O ...

2011

Distinguishing Distributions Using Chernoff Information

Serge Vaudenay, Pouyan Sepehrdad, Thomas Baignères

In this paper, we study the soundness amplification by repetition of cryptographic protocols. As a tool, we use the Chernoff Information. We specify the number of attempts or samples required to distinguish two distributions efficiently in various protocol ...

Springer2010

Saddlepoint approximation for mixture models

Anthony Christopher Davison

Two-component mixture distributions with one component a point mass and the other a continuous density may be used as priors for Bayesian inference when sparse representation of an underlying signal is required. We show how saddlepoint approximation in suc ...

2009