35 citations · 205 across the 22 of their papers we have counts for
8 papers · 1 filter
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
Lorenzo Pacchiardi, Ritabrata Dutta
Bayesian Likelihood-Free Inference methods yield posterior approximations for simulator models with intractable likelihood. Recently, many works trained neural networks to approxim…
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
Sherman Lo, Peter Watson, Peter Dueben +1
The accurate prediction of precipitation is important to allow for reliable warnings of flood or drought risk in a changing climate. However, to make trust-worthy predictions of pr…
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Lorenzo Pacchiardi, Pierre Kunzli, Marcel Schoengens +2
Approximate Bayesian computation (ABC) provides us with a way to infer parameters of models, for which the likelihood function is not available, from an observation. Using ABC, whi…
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi +5
ABCpy is a highly modular scientific library for Approximate Bayesian Computation (ABC) written in Python. The main contribution of this paper is to document a software engineering…
Bayesian inference in hierarchical models by combining independent posteriors
Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally…
Classification and Bayesian Optimization for Likelihood-Free Inference
Michael U. Gutmann, Jukka Corander, Ritabrata Dutta +1
Some statistical models are specified via a data generating process for which the likelihood function cannot be computed in closed form. Standard likelihood-based inference is then…
Likelihood-free inference via classification
Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski +1
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitivel…
Bayes Model Selection with Path Sampling: Factor Models and Other Examples
Ritabrata Dutta, Jayanta K. Ghosh
We prove a theorem justifying the regularity conditions which are needed for Path Sampling in Factor Models. We then show that the remaining ingredient, namely, MCMC for calculatin…