Elements of Sequential Monte Carlo

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Chi tiết về thư mục
Xuất bản năm:	arXiv.org (Dec 5, 2024), p. n/a
Tác giả chính:	Naesseth, Christian A
Tác giả khác:	Lindsten, Fredrik, Schön, Thomas B
Được phát hành:	Cornell University Library, arXiv.org
Những chủ đề:	Recurrent neural networks Probability theory Normalizing (statistics) Bayesian analysis Artificial intelligence Machine learning Statistical analysis Statistical inference Monte Carlo simulation Computer simulation Approximation
Truy cập trực tuyến:	Citation/Abstract Full text outside of ProQuest
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Miêu tả
Bài tóm tắt:	A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximate these intractable expectations. In this tutorial, we review sequential Monte Carlo (SMC), a random-sampling-based class of methods for approximate inference. First, we explain the basics of SMC, discuss practical issues, and review theoretical results. We then examine two of the main user design choices: the proposal distributions and the so called intermediate target distributions. We review recent results on how variational inference and amortization can be used to learn efficient proposals and target distributions. Next, we discuss the SMC estimate of the normalizing constant, how this can be used for pseudo-marginal inference and inference evaluation. Throughout the tutorial we illustrate the use of SMC on various models commonly used in machine learning, such as stochastic recurrent neural networks, probabilistic graphical models, and probabilistic programs.
số ISSN:	2331-8422
Nguồn:	Engineering Database