27 citations · 59 across the 11 of their papers we have counts for
13 papers
Dynamic models using score copula innovations
Landan Zhang, Michael K. Pitt, Robert Kohn
This paper introduces a new class of observation driven dynamic models. The time evolving parameters are driven by innovations of copula form. The resulting models can be made stri…
Large Sample Asymptotics of the Pseudo-Marginal Method
Sebastian M. Schmon, George Deligiannidis, Arnaud Doucet +1
The pseudo-marginal algorithm is a variant of the Metropolis--Hastings algorithm which samples asymptotically from a probability distribution when it is only possible to estimate u…
The Correlated Pseudo-Marginal Method
George Deligiannidis, Arnaud Doucet, Michael K. Pitt
The pseudo-marginal algorithm is a popular variant of the Metropolis--Hastings scheme which allows us to sample asymptotically from a target probability density , when we are on…
Bayesian inference for latent factor GARCH models
Michael K. Pitt, Jamie Hall, Robert Kohn
Latent factor GARCH models are difficult to estimate using Bayesian methods because standard Markov chain Monte Carlo samplers produce slowly mixing and inefficient draws from the…
Annealed Important Sampling for Models with Latent Variables
M. -N. Tran, C. Strickland, M. K. Pitt +1
This paper is concerned with Bayesian inference when the likelihood is analytically intractable but can be unbiasedly estimated. We propose an annealed importance sampling procedur…
Importance sampling squared for Bayesian inference in latent variable models
Minh-Ngoc Tran, Marcel Scharth, Michael K. Pitt +1
We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be unbiasedly estimated. We refer to this procedure as importance samp…