Results 261 to 270 of about 15,911,760 (295)

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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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
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A BAYESIAN MODEL FOR PORTFOLIO SELECTION AND REVISION

The Journal of Finance, 1975
IN PORTFOLIO ANALYSIS, the basic setting is that of an individual or a group of individuals making inferences and decisions in the face of uncertainty about future security prices and related variables. Formal models for decision making under uncertainty require inputs such as probability distributions to reflect a decision maker's uncertainty about ...
Winkler, Robert L, Barry, Christopher B
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Bayesian Model Selection and Model Averaging

Journal of Mathematical Psychology, 2000
This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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COSMOLOGICAL BAYESIAN MODEL SELECTION

Statistical Problems in Particle Physics, Astrophysics and Cosmology, 2006
Bayesian model comparison can be used to decide whether the introduction of a new parameter is warranted by data. I focus on the Savage-Dickey density ratio as a method to compute the Bayes factor of nested models without carrying out a computationally demanding multi-dimensional integration.
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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.
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A fuzzy-Bayesian model for supplier selection

Expert Systems with Applications, 2012
The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making ...
Luciano Ferreira, Denis Borenstein
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Bayesian Model Selection for Pathological Data

2014
The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes ...
Carole H. Sudre, Manuel Jorge Cardoso, Willem H. Bouvy, Geert Jan Biessels, Josephine Barnes, Sébastien Ourselin   +5 more
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Bayesian Model Selection for Diagnostics

2015
Model-Based Diagnosis MBD addresses the task of isolating the most likely fault given a set of system measurements. The model used for diagnostics is critical to this isolation task, yet little work exists for specifying which type of model is best suited to MBD. We apply Bayesian model selection to identify the model that optimizes a diagnostics task,
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Bayesian selection of models of network formation

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017
Models of growing networks have attracted a lot of interest during the past few years. An important question about these models is to decide which model explains an observed network formation most accurately. In this work, we propose a Bayesian model selection scheme which chooses the best model based on predictive distributions.
Lingqing Gan, Petar M. Djuric
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