Alive SMC2: Bayesian model selection for low-count time series models with intractable likelihoods

Biometrics ◽  
2015 ◽  
Vol 72 (2) ◽  
pp. 344-353 ◽  
Author(s):  
Christopher C. Drovandi ◽  
Roy A. McCutchan
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Time Series Models ◽  
Count Time Series ◽  
Selection For

scholarly journals Bayesian Model Selection for Genome-Wide Epistatic Quantitative Trait Loci Analysis

Genetics ◽  
2005 ◽  
Vol 170 (3) ◽  
pp. 1333-1344 ◽  
Author(s):  
Nengjun Yi ◽  
Brian S. Yandell ◽  
Gary A. Churchill ◽  
David B. Allison ◽  
Eugene J. Eisen ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Model Selection ◽  
Quantitative Trait ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Genome Wide ◽  
Trait Loci ◽  
Selection For

scholarly journals Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota

2018 ◽  
Author(s):  
Teresa Portone ◽  
John Henry Niederhaus ◽  
Jason James Sanchez ◽  
Laura Painton Swiler
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Metal Yield ◽  
Selection For

On Bayesian model selection for INGARCH models viatrans-dimensional Markov chain Monte Carlo methods

2021 ◽  
pp. 1471082X2110347
Author(s):  
Panagiota Tsamtsakiri ◽  
Dimitris Karlis
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Bayesian Model ◽  
Linear Models ◽  
Simulation Experiment ◽  
Mean Values ◽  
The Past ◽  
Competing Models ◽  
Selection For ◽  
Log Linear

There is an increasing interest in models for discrete valued time series. Among them, the integer autoregressive conditional heteroscedastic (INGARCH) is a model that has found several applications. In the present article, we study the problem of model selection for this family of models. Namely we consider that an observation conditional on the past follows a Poisson distribution where its mean depends on its past mean values and on past observations. We consider both linear and log-linear models. Our purpose is to select the most appropriate order of such models, using a trans-dimensional Bayesian approach that allows jumps between competing models. A small simulation experiment supports the usage of the method. We apply the methodology to real datasets to illustrate the potential of the approach.

Bayesian model selection for the nonlinear hysteretic model of CLT connections

2020 ◽  
Vol 223 ◽  
pp. 111118 ◽  
Author(s):  
Jixing Cao ◽  
Haibei Xiong ◽  
Feng-Liang Zhang ◽  
Lin Chen ◽  
Carlos Ramonell Cazador
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Hysteretic Model ◽  
Selection For

A guide to Bayesian model selection for ecologists

2015 ◽  
Vol 85 (1) ◽  
pp. 3-28 ◽  
Author(s):  
M. B. Hooten ◽  
N. T. Hobbs
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document