35 citations · 205 across the 22 of their papers we have counts for
26 papers
Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula
David Huk, Rilwan A. Adewoyin, Ritabrata Dutta
This work introduces a novel approach for generating conditional probabilistic rainfall forecasts with temporal and spatial dependence. A two-step procedure is employed. Firstly, m…
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…
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
Rilwan A. Adewoyin, Ritabrata Dutta, Yulan He
In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises R…
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben +1
Probabilistic forecasting relies on past observations to provide a probability distribution for a future outcome, which is often evaluated against the realization using a scoring r…
Bayesian inference of polymerase dynamics over the exclusion process
Massimo Cavallaro, Yuexuan Wang, Daniel Hebenstreit +1
Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along a…
Generalized Bayesian Likelihood-Free Inference
Lorenzo Pacchiardi, Sherman Khoo, Ritabrata Dutta
We propose a posterior for Bayesian Likelihood-Free Inference (LFI) based on generalized Bayesian inference. To define the posterior, we use Scoring Rules (SRs), which evaluate pro…
Score Matched Neural Exponential Families for Likelihood-Free Inference
Lorenzo Pacchiardi, Ritabrata Dutta
Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations. In A…
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…
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Ritabrata Dutta, Karim Zouaoui-Boudjeltia, Christos Kotsalos +6
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffec…
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Rilwan Adewoyin, Peter Dueben, Peter Watson +2
Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and strong precipitation events. However, these numerical simulators have difficulties r…
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic
Ritabrata Dutta, Susana Gomes, Dante Kalise +1
A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounti…
Anomalous Platelet Transport & Fat-Tailed Distributions
Christos Kotsalos, Karim Zouaoui Boudjeltia, Ritabrata Dutta +2
The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation. Here we propose a disruptive view, by showing that the random part of their velocit…
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…
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water
Ritabrata Dutta, Zacharias Faidon Brotzakis, Antonietta Mira
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters a…
Likelihood-free parameter estimation for dynamic queueing networks: case study of passenger flow in an international airport terminal
Anthony Ebert, Ritabrata Dutta, Kerrie Mengersen +3
Dynamic queueing networks (DQN) model queueing systems where demand varies strongly with time, such as airport terminals. With rapidly rising global air passenger traffic placing i…
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…
Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing
Ritabrata Dutta, Bastien Chopard, Jonas Lätt +3
Recent studies show the existing clinical tests to detect Cardio/cerebrovascular diseases (CVD) are ineffectual as they do not consider different stages of platelet activation or t…
Bayesian Inference of Spreading Processes on Networks
Ritabrata Dutta, Antonietta Mira, Jukka-Pekka Onnela
Infectious diseases are studied to understand their spreading mechanisms, to evaluate control strategies and to predict the risk and course of future outbreaks. Because people only…
Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander +2
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called like…
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…
Model Selection and Multiple Testing - A Bayesian and Empirical Bayes Overview and some New Results
Ritabrata Dutta, Malgortaza Bogdan, Jayanta K. Ghosh
We provide a brief overview of both Bayes and classical model selection. We argue tentatively that model selection has at least two major goals, that of finding the correct model o…
Modelling-based experiment retrieval: A case study with gene expression clustering
Paul Blomstedt, Ritabrata Dutta, Sohan Seth +2
Motivation: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art o…
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…