Analyticity of entropy rates of continuous-state hidden Markov models by Tadic, V, Doucet, A

“…The analyticity of the entropy and relative entropy rates of continuous-state hidden Markov models is studied here. Using the analytic continuation principle and the stability properties of the optimal filter, the analyticity of these rates is established for analytically parameterized models. …”

A Monte Carlo algorithm for optimal quantization in hidden Markov models by Tadić, V, Doucet, A

“…In this paper, the problem of the optimal quantization of a signal generated by a hidden Markov model is considered. For this problem, an efficient algorithm based on Monte Carlo sampling, gradient estimation techniques and stochastic approximation is proposed. …”

Simulated annealing for maximum A Posteriori parameter estimation of hidden Markov models by Andrieu, C, Doucet, A

“…Hidden Markov models are mixture models in which the populations from one observation to the next are selected according to an unobserved finite state-space Markov chain. …”

Simulation-based optimal sensor scheduling with application to observer trajectory planning by Singh, S, Kantas, N, Vo, B, Doucet, A, Evans, R

“…The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. …”

Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks by Kantas, N, Singh, S, Doucet, A

“…We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. …”

A lognormal central limit theorem for particle approximations of normalizing constants by Bérard, J, del Moral, P, Doucet, A

“…For particle absorption models in a time-homogeneous environment and hidden Markov models in an ergodic random environment, we also provide more explicit descriptions of the limiting bias and variance.…”

A BACKWARD PARTICLE INTERPRETATION OF FEYNMAN-KAC FORMULAE by Del Moral, P, Doucet, A, Singh, S

“…We also illustrate these results in the context of filtering of hidden Markov models, as well as in computational physics and imaginary time Schroedinger type partial differential equations, with a special interest in the numerical approximation of the invariant measure associated to h-processes. © EDP Sciences, SMAI 2010.…”

Inference on Markov random fields: methods and applications by Lienart, T

“…First, we consider the smoothing problem on Hidden Markov Models (HMMs) and discuss novel algorithms that have sub-quadratic computational complexity in the number of particles used. …”