Results 71 to 80 of about 135,519 (174)

Approximate Bayesian computing for spatial extremes

Computational Statistics & Data Analysis, 2012
Statistical analysis of max-stable processes used to model spatial extremes has been limited by the difficulty in calculating the joint likelihood function. This precludes all standard likelihood-based approaches, including Bayesian approaches. In this paper we present a Bayesian approach through the use of approximate Bayesian computing.
Erhardt, Robert J., Smith, Richard L.
openaire   +3 more sources

Handbook of Approximate Bayesian Computation [PDF]

Journal of the American Statistical Association, 2018
The formation of the Handbook of Approximate Bayesian Computation is a great service to the profession.
openaire   +2 more sources

Using Approximate Bayesian Computation to infer sex ratios from acoustic data.

PLoS ONE, 2018
Population sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex.
Lisa Lehnen, Wigbert Schorcht, Inken Karst, Martin Biedermann, Gerald Kerth, Sebastien J Puechmaille   +5 more
doaj   +1 more source

Using approximate Bayesian computation for estimating parameters in the cue-based retrieval model of sentence processing

MethodsX, 2020
A commonly used approach to parameter estimation in computational models is the so-called grid search procedure: the entire parameter space is searched in small steps to determine the parameter value that provides the best fit to the observed data.
Shravan Vasishth
doaj   +1 more source

Evaluation of mineralogy per geological layers by Approximate Bayesian Computation

, 2019
We propose a new methodology to perform mineralogic inversion from wellbore logs based on a Bayesian linear regression model. Our method essentially relies on three steps.
Bruned, Vianney, Cleynen, Alice, Mas, André, Wlodarczyck, Sylvain   +3 more
core   +3 more sources

Amount of information needed for model choice in Approximate Bayesian Computation.

PLoS ONE, 2014
Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility.
Michael Stocks, Mathieu Siol, Martin Lascoux, Stéphane De Mita   +3 more
doaj   +1 more source

Approximate Integrated Likelihood via ABC methods

, 2014
We propose a novel use of a recent new computational tool for Bayesian inference, namely the Approximate Bayesian Computation (ABC) methodology. ABC is a way to handle models for which the likelihood function may be intractable or even unavailable and/or
Grazian, Clara, Liseo, Brunero
core   +1 more source

HIV with contact-tracing: a case study in Approximate Bayesian Computation

, 2010
Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed for making ...
Cauchemez, Chis Ster, de Arazoza, Finkenstadt, Hsieh, M. G. B. Blum, O'Neill, Streftaris, V. C. Tran   +8 more
core   +4 more sources

Inferring state‐dependent diversification rates using approximate Bayesian computation

Methods in Ecology and Evolution
State‐dependent speciation and extinction (SSE) models are a popular framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny.
Shu Xie, Luis Valente, Rampal S. Etienne
doaj   +1 more source

Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

BMC Bioinformatics, 2020
Background Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge.
Antoine Buetti-Dinh, Malte Herold, Stephan Christel, Mohamed El Hajjami, Francesco Delogu, Olga Ilie, Sören Bellenberg, Paul Wilmes, Ansgar Poetsch, Wolfgang Sand, Mario Vera, Igor V. Pivkin, Ran Friedman, Mark Dopson   +13 more
doaj   +1 more source