Results 41 to 50 of about 862,083 (298)

Enhanced off-grid DOA estimation by corrected power Bayesian inference using difference coarray

The Journal of Engineering, 2019
Sparse Bayesian inference for on-grid direction-of-arrival (DOA) estimation using difference coarray was investigated in the authors’ previous work to estimate more signal sources than the number of physical antenna elements. Sparse Bayesian inference is
Yanan Ma, Xianbin Cao, Xiangrong Wang
doaj   +1 more source

Bayesian Inference in Numerical Cognition: A Tutorial Using JASP

Journal of Numerical Cognition, 2020
Researchers in numerical cognition rely on hypothesis testing and parameter estimation to evaluate the evidential value of data. Though there has been increased interest in Bayesian statistics as an alternative to the classical, frequentist approach to ...
Thomas J. Faulkenberry, Alexander Ly, Eric-Jan Wagenmakers   +2 more
doaj   +1 more source

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.
openaire   +3 more sources

Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution

Stats, 2022
In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and ...
Tzong-Ru Tsai, Hua Xin, Ya-Yen Fan, Yuhlong Lio   +3 more
doaj   +1 more source

Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]

PLoS ONE, 2014
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
doaj   +1 more source

Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Journal of Statistical Software, 2022
Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models,
Alexandre Bouchard-Côté, Kevin Chern, Davor Cubranic, Sahand Hosseini, Justin Hume, Matteo Lepur, Zihui Ouyang, Giorgio Sgarbi   +7 more
doaj   +1 more source

Bayesian and maximum likelihood phylogenetic analyses of protein sequence data under relative branch-length differences and model violation

BMC Evolutionary Biology, 2005
Background Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets.
Harlow Timothy J, Mar Jessica C, Ragan Mark A   +2 more
doaj   +1 more source

HagesLab/Bayesian-Inference-TRPL: Final Publication Version

, 2022
This is the state of Bayesian-Inference-TRPL as of acceptance of our Joule article and is the version of the code used to calculate all inferences presented in that ...
cfai2304, charleshages
core   +1 more source

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
doaj   +1 more source

The Inverse of Exact Renormalization Group Flows as Statistical Inference

Entropy
We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection–diffusion equation.
David S. Berman, Marc S. Klinger
doaj   +1 more source