Results 31 to 40 of about 864,389 (286)

Model selection in cosmology [PDF]

, 2006
Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters.
Andrew Liddle, David Parkinson, Jeffreys H, Mukherjee P, Pia Mukherjee, Skilling J   +5 more
core   +1 more source

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition [PDF]

, 2014
This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition.
Huemmer, Christian, Kellermann, Walter, Maas, Roland, Sehr, Armin   +3 more
core   +3 more sources

Model-averaged Bayesian t tests

Psychonomic Bulletin & Review, 2022
Abstract One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where
Maximilian Maier, František Bartoš, Daniel S Quintana, Fabian Dablander, Don van den Bergh, Maarten Marsman, Alexander Ly, Eric-Jan Wagenmakers   +7 more
openaire   +2 more sources

The Bayesian boom: good thing or bad? [PDF]

, 2014
A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from
Anderson, Armendt, Bates, Binmore, Bowers, Bowers, Brier, Brown, Busemeyer, Busemeyer, Camerer, Chater, Chater, Chater, Chomsky, Chomsky, Corner, Corner, Corner, Cox, Doucet, Earman, Edgington, Edwards, Elqayam, Elqayam, Evans, Ferejohn, Fodor, Fox, Friedman, Friedman, Geisler, Geman, Gigerenzer, Gold, Govier, Griffiths, Griffiths, Hahn, Hahn, Hahn, Hahn, Hahn, Hahn, Hahn, Hajek, Harris, Harris, Hilton, Howes, Howson, Jarvstad, Jones, Joyce, Kahneman, Karni, Kemp, Klauer, Klayman, Kuhn, Lakatos, Lehtinen, Leitgeb, Leitgeb, Lewandowsky, Neuman, Newell, Oaksford, Oaksford, Oaksford, Oaksford, Oberauer, Perfors, Peterson, Popper, Pothos, Pothos, Pothos, Pothos, Rabin, Ripley, Rosenkrantz, Sanborn, Satz, Savage, Schroyens, Seidenberg, Skyrms, Starmer, Teller, Tentori, Thaler, Tversky, Von Neumann, Wakker, Wason, Wharton, Winkler, Yuille   +99 more
core   +3 more sources

Hierarchical Graphical Bayesian Models in Psychology

Revista Colombiana de Estadística, 2014
The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological ...
GUILLERMO CAMPITELLI, GUILLERMO MACBETH
doaj   +1 more source

Local Bayesian Dirichlet mixing of imperfect models

Scientific Reports, 2023
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz   +2 more
doaj   +1 more source

Bayesian Model Monitoring

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1986
SUMMARY A simple method of monitoring the predictive performance of a class of Bayesian models is introduced. The models involve sequential analyses of sequences of observations and are appropriate for a variety of monitoring and forecasting applications.
openaire   +2 more sources

Bayesian models of object perception [PDF]

Current Opinion in Neurobiology, 2003
The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life.
Kersten, Daniel, Yuille, Alan L
openaire   +5 more sources

Bayesian explanatory additive IRT models

British Journal of Mathematical and Statistical Psychology, 2021
In this article we extend the framework of explanatory mixed IRT models to a more general class called explanatory additive IRT models. We do this by augmenting the linear predictors in terms of smooth functions. This development offers many new modeling options such as the inclusion of nonlinear covariate effects, the specification of various temporal
Patrick Mair, Kathrin Gruber
openaire   +2 more sources

Bayesian analysis of CCDM Models

, 2017
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, leads to negative creation pressure, which can be used to explain the accelerated expansion of the Universe.
Andrade-Oliveira, F., Jesus, J. F., Valentim, R.   +2 more
core   +2 more sources