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Related papers: Partition MCMC for inference on acyclic digraphs

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We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution. Broadly, the method rewrites the Multilevel…

Methodology · Statistics 2022-09-05 Mikkel B. Lykkegaard , Tim J. Dodwell , Colin Fox , Grigorios Mingas , Robert Scheichl

We consider the problem of Bayesian inference for changepoints where the number and position of the changepoints are both unknown. In particular, we consider product partition models where it is possible to integrate out model parameters…

Computation · Statistics 2017-03-14 Alan Benson , Nial Friel

We present a novel Bayesian inference tool that uses a neural network to parameterise efficient Markov Chain Monte-Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of…

Cosmology and Nongalactic Astrophysics · Physics 2020-06-03 Adam Moss

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

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such…

Machine Learning · Statistics 2021-10-05 Theodore Papamarkou , Jacob Hinkle , M. Todd Young , David Womble

Network reconstruction is the task of inferring the unseen interactions between elements of a system, based only on their behavior or dynamics. This inverse problem is in general ill-posed, and admits many solutions for the same…

Machine Learning · Statistics 2025-03-12 Tiago P. Peixoto

In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learned causal model is commonly represented as a partially…

Artificial Intelligence · Computer Science 2023-03-01 Malte Luttermann , Marcel Wienöbst , Maciej Liśkiewicz

Causal learning from data has received much attention recently. Bayesian networks can be used to capture causal relationships. There, one recovers a weighted directed acyclic graph in which random variables are represented by vertices, and…

Machine Learning · Computer Science 2026-01-06 Pavel Rytir , Ales Wodecki , Georgios Korpas , Jakub Marecek

Bayesian inverse problems often involve sampling posterior distributions on infinite-dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are characterized by deteriorating mixing times upon mesh-refinement,…

Computation · Statistics 2017-03-08 Alexandros Beskos , Mark Girolami , Shiwei Lan , Patrick E. Farrell , Andrew M. Stuart

This work addresses the NP-Hard problem of acyclic directed acyclic graph (DAG) partitioning problem. The acyclic partitioning problem is defined as partitioning the vertex set of a given directed acyclic graph into disjoint and…

Data Structures and Algorithms · Computer Science 2022-07-28 M. Yusuf Özkaya , Ümit V. Çatalyürek

We consider the problem of fully Bayesian posterior estimation and uncertainty quantification in undirected Gaussian graphical models via Markov chain Monte Carlo (MCMC) under recently-developed element-wise graphical priors, such as the…

Methodology · Statistics 2026-03-23 Zejin Gao , Ksheera Sagar , Anindya Bhadra

In the quest to improve efficiency, interdependence and complexity are becoming defining characteristics of modern complex networks representing engineered and natural systems. Graph theory is a widely used framework for modeling such…

Social and Information Networks · Computer Science 2022-05-31 Sai Munikoti , Laya Das , Balasubramaniam Natarajan

The use of complex networks as a modern approach to understanding the world and its dynamics is well-established in literature. The adjacency matrix, which provides a one-to-one representation of a complex network, can also yield several…

Social and Information Networks · Computer Science 2023-01-23 Mariane B. Neiva , Odemir M. Bruno

We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables. LDAGs generalize earlier proposals for allowing local structures in the conditional probability distribution of a node, such…

Machine Learning · Statistics 2014-11-12 Johan Pensar , Henrik Nyman , Timo Koski , Jukka Corander

In many modern applications, there is interest in analyzing enormous data sets that cannot be easily moved across computers or loaded into memory on a single computer. In such settings, it is very common to be interested in clustering.…

Computation · Statistics 2020-05-15 Hanyu Song , Yingjian Wang , David B. Dunson

This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a…

Machine Learning · Statistics 2023-11-28 Seyong Hwang , Kyoungjae Lee , Sunmin Oh , Gunwoong Park

Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtained, calling for more advanced methods to its estimation.…

Signal Processing · Electrical Eng. & Systems 2023-03-14 Andrei Buciulea , Antonio G. Marques

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to…

Computer Vision and Pattern Recognition · Computer Science 2018-11-30 Adam Kortylewski , Mario Wieser , Andreas Morel-Forster , Aleksander Wieczorek , Sonali Parbhoo , Volker Roth , Thomas Vetter

Decision trees have found widespread application within the machine learning community due to their flexibility and interpretability. This paper is directed towards learning decision trees from data using a Bayesian approach, which is…

Machine Learning · Computer Science 2023-12-05 Jodie A. Cochrane , Adrian G. Wills , Sarah J. Johnson

An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the…

Machine Learning · Computer Science 2012-07-19 Eric P. Xing , Michael I. Jordan , Stuart Russell
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