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Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the CTMC state space expands,…

Methodology · Statistics 2026-02-09 Filippo Monti , Xiang Ji , Marc A. Suchard

This paper presents a Markov chain Monte Carlo method to generate approximate posterior samples in retrospective multiple changepoint problems where the number of changes is not known in advance. The method uses conjugate models whereby the…

Computation · Statistics 2010-11-15 Jason Wyse , Nial Friel

Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees, and formulate learning as posterior…

Machine Learning · Statistics 2013-08-26 Balaji Lakshminarayanan , Daniel M. Roy , Yee Whye Teh

The availability of data sets with large numbers of variables is rapidly increasing. The effective application of Bayesian variable selection methods for regression with these data sets has proved difficult since available Markov chain…

Computation · Statistics 2019-05-08 Jim Griffin , Krys Latuszynski , Mark Steel

Recent advances on overfitting Bayesian mixture models provide a solid and straightforward approach for inferring the underlying number of clusters and model parameters in heterogeneous datasets. The applicability of such a framework in…

Methodology · Statistics 2018-03-29 Panagiotis Papastamoulis

When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input variables X. We consider Bayesian models for this task. In…

Machine Learning · Computer Science 2013-02-08 David Heckerman , Christopher Meek

Bayesian network (BN) modelling is extensively used in systems epidemiology. Usually it consists in selecting and reporting the best-fitting structure conditional to the data. A major practical concern is avoiding overfitting, on account of…

Computation · Statistics 2019-02-19 Gilles Kratzer , Reinhard Furrer

Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Carlo. State-of-the-art developments involve representing MJP…

Computation · Statistics 2019-04-18 Iker Perez , Theodore Kypraios

Bayesian variable selection requires sampling from a posterior distribution that combines discrete model indicators with continuously varying parameters, a challenge often addressed through reversible jump Markov chain Monte Carlo (RJMCMC).…

Methodology · Statistics 2026-05-01 Don van den Bergh , Merlise A. Clyde , Adrian E. Raftery , Maarten Marsman

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables…

Methodology · Statistics 2013-01-14 Jared S. Murray , David B. Dunson , Lawrence Carin , Joseph E. Lucas

A central statistical problem in population genetics is to infer evolutionary and biological parameters such as the strength of natural selection and allele age from DNA samples extracted from a contemporary population. That all samples…

Populations and Evolution · Quantitative Biology 2025-02-19 Jaromir Sant , Paul A. Jenkins , Jere Koskela , Dario Spano

The Markov Chain Monte Carlo (MCMC) methods are popular when considering sampling from a high-dimensional random variable $\mathbf{x}$ with possibly unnormalised probability density $p$ and observed data $\mathbf{d}$. However, MCMC requires…

Computation · Statistics 2020-03-11 Haoyun Ying , Keheng Mao , Klaus Mosegaard

We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian…

Methodology · Statistics 2017-07-18 Clara Grazian , Brunero Liseo

It is a well known fact that recovery rates tend to go down when the number of defaults goes up in economic downturns. We demonstrate how the loss given default model with the default and recovery dependent via the latent systematic risk…

Risk Management · Quantitative Finance 2014-11-03 Xiaolin Luo , Pavel V. Shevchenko

Advances in digital sensors, digital data storage and communications have resulted in systems being capable of accumulating large collections of data. In the light of dealing with the challenges that massive data present, this work proposes…

Computation · Statistics 2015-12-09 Allan De Freitas , François Septier , Lyudmila Mihaylova

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a…

Artificial Intelligence · Computer Science 2007-05-23 V. Schetinin , J. E. Fieldsend , D. Partridge , W. J. Krzanowski , R. M. Everson , T. C. Bailey , A. Hernandez

In this paper we propose a copula contagion mixture model for correlated default times. The model includes the well known factor, copula, and contagion models as its special cases. The key advantage of such a model is that we can study the…

Pricing of Securities · Quantitative Finance 2010-10-21 Harry Zheng

Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC).…

Machine Learning · Statistics 2021-06-21 Antonio Khalil Moretti , Liyi Zhang , Christian A. Naesseth , Hadiah Venner , David Blei , Itsik Pe'er

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely…

Computation · Statistics 2008-10-08 Y. Fan , D. S. Leslie , M. P. Wand

A Bayesian approach to the classification problem is proposed in which random partitions play a central role. It is argued that the partitioning approach has the capacity to take advantage of a variety of large-scale spatial structures, if…

Statistics Theory · Mathematics 2007-06-13 Marc A. Coram