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Bayesian Inference for Inverse Problems [PDF]

AIP Conference Proceedings, 2002
Inverse problems arise everywhere we have indirect measurement. Regularization and Bayesian inference methods are two main approaches to handle inverse problems. Bayesian inference approach is more general and has much more tools for developing efficient methods for difficult problems.
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Fuzzy Bayesian Inference [PDF]

, 2009
Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Moreover a-priori information in Bayesian inference is usually not available as a precise probability distribution. In case of fuzzy data and fuzzy a-priori information Bayes' theorem has to be generalized.
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Bayesian Nonparametric Hidden Semi-Markov Models

, 2012
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data.
Johnson, Matthew J., Willsky, Alan S.
core  

Introduction to Bayesian Inference

, 2023
An introductory lecture on Bayesian inference by Tracy A. Heath (http://phyloworks.org/). Some content is from other authors and credited where necessary. This lecture is given at various workshops on molecular evolution and phylogenetics.
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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

, 2018
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates ...
Broderick, Tamara, Campbell, Trevor, Huggins, Jonathan H., Kasprzak, Mikołaj   +3 more
core  

Bayesian inference and the parametric bootstrap

The Annals of Applied Statistics, 2012
The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families and are particularly simple starting from Jeffreys invariant prior. Because of the i.i.d.
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Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model. [PDF]

PLoS Comput Biol
Roberts I, Everitt RG, Koskela J, Didelot X.   +3 more
europepmc   +1 more source

Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling. [PDF]

Bioinform Biol Insights
Le MH, Dao NA, Dang XT.
europepmc   +1 more source

An FPGA implementation of Bayesian inference with spiking neural networks. [PDF]

Front Neurosci, 2023
Li H, Wan B, Fang Y, Li Q, Liu JK, An L.
europepmc   +1 more source

Changes in facial expressions can distinguish Parkinson's disease via Bayesian inference. [PDF]

Front Neurol
Mouse M, Gong H, Liu Y, Xu F, Zou X, Huang M, Yang X.   +6 more
europepmc   +1 more source