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Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in…

Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by…

Machine Learning · Computer Science 2015-01-23 Diederik P. Kingma , Max Welling

Bayesian meta-learning enables robust and fast adaptation to new tasks with uncertainty assessment. The key idea behind Bayesian meta-learning is empirical Bayes inference of hierarchical model. In this work, we extend this framework to…

Machine Learning · Computer Science 2020-11-19 Yayi Zou , Xiaoqi Lu

The methodology developed in this article is motivated by a wide range of prediction and uncertainty quantification problems that arise in Statistics, Machine Learning and Applied Mathematics, such as non-parametric regression, multi-class…

Methodology · Statistics 2019-03-26 Victor Chen , Matthew M. Dunlop , Omiros Papaspiliopoulos , Andrew M. Stuart

Bayes linear analysis and approximate Bayesian computation (ABC) are techniques commonly used in the Bayesian analysis of complex models. In this article we connect these ideas by demonstrating that regression-adjustment ABC algorithms…

Methodology · Statistics 2012-12-10 D. J. Nott , Y. Fan , L. Marshall , S. A. Sisson

To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed as a product of terms, each associated with a subset of the…

Computation · Statistics 2020-04-09 Lewis J. Rendell , Adam M. Johansen , Anthony Lee , Nick Whiteley

We propose a Bayesian inference framework to estimate uncertainties in inverse scattering problems. Given the observed data, the forward model and their uncertainties, we find the posterior distribution over a finite parameter field…

Numerical Analysis · Mathematics 2020-11-17 Ana Carpio , Sergei Iakunin , Georg Stadler

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is…

Machine Learning · Statistics 2019-02-27 Shubhanshu Shekhar , Tara Javidi

Hierarchical learning models, such as mixture models and Bayesian networks, are widely employed for unsupervised learning tasks, such as clustering analysis. They consist of observable and hidden variables, which represent the given data…

Machine Learning · Statistics 2018-01-08 Keisuke Yamazaki

Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contraction. The continual use of EMG-based interfaces gradually…

Signal Processing · Electrical Eng. & Systems 2023-10-03 Seitaro Yoneda , Akira Furui

In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex…

Machine Learning · Computer Science 2023-11-10 Anshuk Uppal , Kristoffer Stensbo-Smidt , Wouter Boomsma , Jes Frellsen

In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice,…

Computation · Statistics 2017-02-07 Alexandros Beskos , Ajay Jasra , Kody Law , Raul Tempone , Yan Zhou

We develop a formalism to study the use of Level Set Method (LSM) in the investigation of evolution of observables in terms of parameters of the Hamiltonian, both of the system itself and the control part. A simple example with an analytic…

Quantum Physics · Physics 2007-05-23 Fariel Shafee

Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian…

Numerical Analysis · Mathematics 2026-05-26 Jakob Scheffels , Elizabeth Qian , Iason Papaioannou , Elisabeth Ullmann

Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their…

In Bayesian inverse problems, the posterior distribution is used to quantify uncertainty about the reconstructed solution. In practice, Markov chain Monte Carlo algorithms often are used to draw samples from the posterior distribution.…

Numerical Analysis · Mathematics 2018-03-13 D. Andrew Brown , Arvind Saibaba , Sarah Vallélian

As compared to typical mobile manipulation tasks, sequential mobile manipulation poses a unique challenge -- as the robot operates over extended periods, successful task completion is not solely dependent on consistent motion generation but…

Robotics · Computer Science 2026-03-12 Xintong Du , Jingxing Qian , Siqi Zhou , Angela P. Schoellig

One fundamental problem when solving inverse problems is how to find regularization parameters. This article considers solving this problem using data-driven bilevel optimization, i.e. we consider the adaptive learning of the regularization…

Statistics Theory · Mathematics 2021-01-08 Neil K. Chada , Claudia Schillings , Xin T. Tong , Simon Weissmann

With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already…

Methodology · Statistics 2019-05-14 Lu Zhang , Abhirup Datta , Sudipto Banerjee

Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-making processes in various fields such as economics,…

Machine Learning · Computer Science 2024-12-25 Omer Ekmekcioglu , Nursen Aydin , Juergen Branke