Results 21 to 30 of about 43,983 (273)

Importance-Weighted Variational Inference Model Estimation for Offline Bayesian Model-Based Reinforcement Learning

IEEE Access, 2023
This paper proposes a model estimation method in offline Bayesian model-based reinforcement learning (MBRL). Learning a Bayes-adaptive Markov decision process (BAMDP) model using standard variational inference often suffers from poor predictive ...
Toru Hishinuma, Kei Senda
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A tutorial on variational Bayesian inference

Artificial Intelligence Review, 2011
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense.
Fox, C, Roberts, S
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Bayesian Inference-Based Energy Management Strategy for Techno-Economic Optimization of a Hybrid Microgrid

Energies, 2023
This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid.
Abdellah Benallal, Nawal Cheggaga, Adrian Ilinca, Selma Tchoketch-Kebir, Camelia Ait Hammouda, Noureddine Barka   +5 more
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A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference

Applied Sciences, 2021
In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the
Ping Dong, Jianhua Cheng, Liqiang Liu
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Direction of arrival estimation under Class A modelled noise in shallow water using variational Bayesian inference method

IET Radar, Sonar & Navigation, 2022
The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption.
Xiao Feng, Xuebo Zhang, Ruiping Song, Junfeng Wang, Haixin Sun, Hamada Esmaiel   +5 more
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Variationally Inferred Sampling through a Refined Bound

Entropy, 2021
In this work, a framework to boost the efficiency of Bayesian inference in probabilistic models is introduced by embedding a Markov chain sampler within a variational posterior approximation.
Víctor Gallego, David Ríos Insua
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Variational Bayesian Inference with Stochastic Search [PDF]

, 2012
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)
John W. Paisley, David M. Blei, Michael I. Jordan   +2 more
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Variational Inference over Nonstationary Data Streams for Exponential Family Models

Mathematics, 2020
In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons.
Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen   +4 more
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Universal Darwinism as a process of Bayesian inference

Frontiers in Systems Neuroscience, 2016
Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these ...
John Oberon Campbell
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Variational Bayesian Sparse Signal Recovery With LSM Prior

IEEE Access, 2017
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang, Yongxiang Liu, Xiang Li, Guoan Bi   +3 more
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