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A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture…

Machine Learning · Statistics 2023-07-03 Adrian G. Wills , Johannes Hendriks , Christopher Renton , Brett Ninness

We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties…

Programming Languages · Computer Science 2019-07-16 Feras A. Saad , Marco F. Cusumano-Towner , Ulrich Schaechtle , Martin C. Rinard , Vikash K. Mansinghka

We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo methods, which converge slowly and are unreliable for…

Machine Learning · Computer Science 2019-03-04 Henry Chai , Jean-Francois Ton , Roman Garnett , Michael A. Osborne

Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming-graphical models include Bayesian networks and factor graphs. In this paper we develop a new model of…

Artificial Intelligence · Computer Science 2022-01-20 Albert Benveniste , Jean-Baptiste Raclet

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is…

Computation · Statistics 2018-08-03 Jonathan U Harrison , Ruth E Baker

We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bi-directed edges, is a representation of conditional…

Methodology · Statistics 2012-07-02 Ricardo Silva , Zoubin Ghahramani

In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model…

Machine Learning · Statistics 2018-05-03 Nicolo Fusi , Rishit Sheth , Huseyn Melih Elibol

Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model the unknown function from controller parameters to a…

Systems and Control · Computer Science 2019-01-24 Matthias Neumann-Brosig , Alonso Marco , Dieter Schwarzmann , Sebastian Trimpe

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for…

Methodology · Statistics 2015-03-14 Paul Fearnhead , Dennis Prangle

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction…

Computer Vision and Pattern Recognition · Computer Science 2012-07-03 Young Jun Ko , Matthias Seeger

We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models…

Methodology · Statistics 2024-03-29 Dawid Bernaciak , Jim E. Griffin

Motivated by examples from genetic association studies, this paper considers the model selection problem in a general complex linear model system and in a Bayesian framework. We discuss formulating model selection problems and incorporating…

Methodology · Statistics 2014-03-14 Xiaoquan Wen

Analyzing demographic data collected across multiple populations, time periods, and age groups is challenging due to the interplay of high dimensionality, demographic heterogeneity among groups, and stochastic variability within smaller…

Applications · Statistics 2025-12-12 Gregor Zens

This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous…

Methodology · Statistics 2020-04-27 Dimitris Korobilis

Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific…

Methodology · Statistics 2022-11-08 Aditi Shenvi , Silvia Liverani

The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters that are typically selected by Bayesian optimization or cross-validation. This…

Machine Learning · Statistics 2023-10-09 Eliezer de Souza da Silva , Tomasz Kuśmierczyk , Marcelo Hartmann , Arto Klami

Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. However, the graph used by these algorithms is often…

Machine Learning · Statistics 2020-06-25 Soumyasundar Pal , Saber Malekmohammadi , Florence Regol , Yingxue Zhang , Yishi Xu , Mark Coates

Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal…

Databases · Computer Science 2022-05-31 Amir Pouya Aghasadeghi , Jan Van den Bussche , Julia Stoyanovich

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to…

Quantitative Methods · Quantitative Biology 2018-12-10 Yen Ting Lin , Nicolas E. Buchler

Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood. Eliciting meaningful priors on the weight space of modern overparameterized models is notoriously difficult, and misspecification…

Machine Learning · Computer Science 2026-05-19 Boning Zhang , Matteo Zecchin , Mingzhao Guo , Dongzhu Liu , Osvaldo Simeone