Results 251 to 260 of about 157,395 (292)

Bayesian joint and individual component regression for multigroup physiological data. [PDF]

Sci Rep
Kara M, Cengiz MA, Dünder E, Şenel T.
europepmc   +1 more source

NbBayesLM: bayesian prediction of nanobody thermostability using protein language model. [PDF]

Front Bioinform
Shishir FS, Rudro R, Sarker B, Zhong C, Shomaji S.   +4 more
europepmc   +1 more source

Experiment-based calibration: Inference and decision-making. [PDF]

Behav Res Methods
Mancinelli F, Bach DR.
europepmc   +1 more source

Interpretable Bayesian optimization for catalyst discovery.

Faraday Discuss
Nair AS, Foppa L, Scheffler M.
europepmc   +1 more source

Bayesian Model Selection with an Uninformative Prior*

Oxford Bulletin of Economics and Statistics, 2003
AbstractBayesian model selection with posterior probabilities and no subjective prior information is generally not possible because of the Bayes factors being ill‐defined. Using careful consideration of the parameter of interest in cointegration analysis and a re‐specification of the triangular model of Phillips (Econometrica, Vol. 59, pp.
Strachan, Rodney W., van Dijk, Herman K.
openaire   +6 more sources

Bayesian Model Selection for Heteroskedastic Models

SSRN Electronic Journal, 2008
It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models.
Chen, Cathy W.S., Gerlach, Richard H., So, Mike K.P.   +2 more
openaire   +2 more sources

Bayesian Model Selection and Statistical Modeling

, 2010
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model.
Tomohiro Ando, Ando, Tomohiro
openaire   +2 more sources

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Bayesian Post-Model-Selection Estimation

IEEE Signal Processing Letters, 2021
Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation.
Nadav Harel, Tirza Routtenberg
openaire   +1 more source

Bayesian model selection in ARFIMA models

Expert Systems with Applications, 2010
Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both
Erol Egrioglu, Süleyman Günay
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