An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and the inversion of detector effects. We provide an overview of the Bayesian and frequentist statistical frameworks, describe how machine-learning-based SBI methods, such as neural posterior estimation and neural likelihood estimation, can be used for parameter estimation within these frameworks, and show that the same methods can also be applied to Empirical Bayes or unfolding tasks. We also discuss how to validate inference results and the limitations of SBI with machine learning.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Frequentist Statistical Introduction to Variational Inference, Autoencoders, and Diffusion Models
While Variational Inference (VI) is central to modern generative models like Variational Autoencoders (VAEs) and Denoising Diffusion Models (DDMs), its pedagogical treatment is split across disciplines. In statistics, VI…
Bayesian Neural Networks: An Introduction and Survey
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natu…
BIG-bench Machine Learningregressionspeech-recognitionSpeech Recognition+1A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning probl…
BIG-bench Machine LearningBernstein-von Mises for Adaptively Collected Data
Uncertainty quantification (UQ) for adaptively collected data, such as that coming from adaptive experiments, bandits, or reinforcement learning, is necessary for critical elements of data collection such as ensuring saf…
Reinforcement LearningDebiased Bayesian Inference for High-dimensional Regression Models
There has been significant progress in Bayesian inference based on sparsity-inducing (e.g., spike-and-slab and horseshoe-type) priors for high-dimensional regression models. The resulting posteriors, however, in general …
Bayesian Inference