442 citations · 3.3k across the 135 of their papers we have counts for
8 papers · 1 filter
Bias of Particle Approximations to Optimal Filter Derivative
Vladislav Z. B. Tadic, Arnaud Doucet
In many applications, a state-space model depends on a parameter which needs to be inferred from a data set. Quite often, it is necessary to perform the parameter inference online.…
Asymptotic Properties of Recursive Maximum Likelihood Estimation in Non-Linear State-Space Models
Vladislav Z. B. Tadic, Arnaud Doucet
Using stochastic gradient search and the optimal filter derivative, it is possible to perform recursive (i.e., online) maximum likelihood estimation in a non-linear state-space mod…
Asymptotic Bias of Stochastic Gradient Search
Vladislav B. Tadic, Arnaud Doucet
The asymptotic behavior of the stochastic gradient algorithm with a biased gradient estimator is analyzed. Relying on arguments based on the dynamic system theory (chain-recurrence…
Sequentially interacting Markov chain Monte Carlo methods
Anthony Brockwell, Pierre Del Moral, Arnaud Doucet
Sequential Monte Carlo (SMC) is a methodology for sampling approximately from a sequence of probability distributions of increasing dimension and estimating their normalizing const…
On adaptive resampling strategies for sequential Monte Carlo methods
Pierre Del Moral, Arnaud Doucet, Ajay Jasra
Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and…
On nonlinear Markov chain Monte Carlo
Christophe Andrieu, Ajay Jasra, Arnaud Doucet +1
Let be the space of probability measures on a measurable space . In this paper we introduce a class of nonlinear Markov chain Monte Carlo (MCMC) m…