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Multilevel Delayed Acceptance MCMC
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.
Methodology Computation 62F15, 62M05, 65C05, 65C40
ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
Liesel: A Probabilistic Programming Framework for Developing Semi-Parametric Regression Models and Custom Bayesian Inference Algorithms
Liesel is a new probabilistic programming framework developed with the aim of supporting research on Bayesian inference based on Markov chain Monte Carlo (MCMC) simulations in general and semi-parametric regression specifications in particular.
Computation
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.
Functional probabilistic programming for scalable Bayesian modelling
This paper introduces functional programming principles which can be used to develop an embedded probabilistic programming language.
Computation
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
An Introduction to Probabilistic Programming
We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.
