17 citations · 21 across the 5 of their papers we have counts for
5 papers
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
Pierre Del Moral, Arnaud Doucet, Sumeetpal Singh
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many ap…
Asymptotic Behaviour of Approximate Bayesian Estimators
Thomas A. Dean, Sumeetpal S. Singh
Although approximate Bayesian computation (ABC) has become a popular technique for performing parameter estimation when the likelihood functions are analytically intractable there…
Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
Thomas A. Dean, Sumeetpal S. Singh, Ajay Jasra +1
Approximate Bayesian computation (ABC) is a popular technique for approximating likelihoods and is often used in parameter estimation when the likelihood functions are analytically…
Forward Smoothing using Sequential Monte Carlo
Pierre Del Moral, Arnaud Doucet, Sumeetpal Singh
Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to com…
A Backward Particle Interpretation of Feynman-Kac Formulae
Pierre Del Moral, Arnaud Doucet, Sumeetpal S. Singh
We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretati…