4 citations · 6 across the 5 of their papers we have counts for
6 papers
The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inference
Pablo Montero-Manso, Marcel Scharth
We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining. Analysts specify a generative world, a d…
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan +3
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments al…
Markov Interacting Importance Samplers
Eduardo F. Mendes, Marcel Scharth, Robert Kohn
We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) appro…
Particle Efficient Importance Sampling
Marcel Scharth, Robert Kohn
The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integr…
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…
On the existence of moments for high dimensional importance sampling
Michael K. Pitt, Minh-Ngoc Tran, Marcel Scharth +1
Theoretical results for importance sampling rely on the existence of certain moments of the importance weights, which are the ratios between the proposal and target densities. In p…