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08/18/2023
Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula
This work introduces a novel approach for generating conditional probabi...
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05/31/2022
Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
Bayesian Likelihood-Free Inference methods yield posterior approximation...
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05/25/2022
RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
In this paper, we study the task of improving the cohesion and coherence...
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12/15/2021
Probabilistic Forecasting with Conditional Generative Networks via Scoring Rule Minimization
Probabilistic forecasting consists of stating a probability distribution...
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09/10/2021
Bayesian inference of PolII dynamics over the exclusion process
Transcription is a complex phenomenon that permits the conversion of gen...
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04/08/2021
Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators
We propose a framework for Bayesian Likelihood-Free Inference (LFI) base...
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12/20/2020
Score Matched Conditional Exponential Families for Likelihood-Free Inference
To perform Bayesian inference for stochastic simulator models for which ...
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12/17/2020
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
The accurate prediction of precipitation is important to allow for relia...
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10/13/2020
Interpretable pathological test for Cardio-vascular disease: Approximate Bayesian computation with distance learning
Cardio/cerebrovascular diseases (CVD) have become one of the major healt...
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08/20/2020
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Climate models (CM) are used to evaluate the impact of climate change on...
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06/29/2020
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England
A mathematical model for the COVID-19 pandemic spread in England is pres...
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09/28/2019
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption
Approximate Bayesian computation (ABC) provides us with a way to infer p...
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04/08/2018
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water
Molecular dynamics (MD) simulations give access to equilibrium and/or dy...
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04/07/2018
Likelihood-Free Parameter Estimation for Dynamic Queueing Networks
Many complex real-world systems such as airport terminals, manufacturing...
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11/13/2017
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
ABCpy is a highly modular scientific library for Approximate Bayesian Co...
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10/03/2017
Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing
A numerical model that quantitatively describes how platelets in a shear...
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09/26/2017
Bayesian Inference of Spreading Processes on Networks
Infectious diseases are studied to understand their spreading mechanisms...
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09/26/2017
Bayesian Inference of Network Epidemics
Infectious diseases are studied to understand their spreading mechanisms...
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03/30/2016
Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets ...
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05/19/2015
Modelling-based experiment retrieval: A case study with gene expression clustering
Motivation: Public and private repositories of experimental data are gro...
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02/19/2015
Classification and Bayesian Optimization for Likelihood-Free Inference
Some statistical models are specified via a data generating process for ...
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07/18/2014
Likelihood-free inference via classification
Increasingly complex generative models are being used across disciplines...
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10/08/2013