Results 231 to 240 of about 344,945 (269)
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Statistics and Computing, 1998
A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995).
David G. T. Denison +2 more
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A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995).
David G. T. Denison +2 more
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SSRN Electronic Journal, 2005 This paper presents a modification of the inspection game: The ?Bayesian Monitoring? model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgements on an informative but imperfect signal which can be generated costlessly.
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Synthese, 2017
There is a distinction between merely having the right belief, and further basing that belief on the right reasons. Any adequate epistemology needs to be able to accommodate the basing relation that marks this distinction. However, trouble arises for Bayesianism.
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There is a distinction between merely having the right belief, and further basing that belief on the right reasons. Any adequate epistemology needs to be able to accommodate the basing relation that marks this distinction. However, trouble arises for Bayesianism.
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Synthese, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
William L. Harper +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
William L. Harper +2 more
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International Journal on Artificial Intelligence Tools, 2005
Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of ...
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Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of ...
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Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
We show that several important Bayesian bounds studied in machine learning, both in the batch as well as the online setting, arise by an application of a simple compression lemma. In particular, we derive (i) PAC-Bayesian bounds in the batch setting, (ii) Bayesian log-loss bounds and (iii) Bayesian bounded-loss bounds in the online setting using the ...
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We show that several important Bayesian bounds studied in machine learning, both in the batch as well as the online setting, arise by an application of a simple compression lemma. In particular, we derive (i) PAC-Bayesian bounds in the batch setting, (ii) Bayesian log-loss bounds and (iii) Bayesian bounded-loss bounds in the online setting using the ...
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Bayesian Robustness and Bayesian Nonparametrics
2000Bayesian robustness studies the sensitivity of Bayesian answers to user inputs, especially to the specification of the prior. Nonparametric Bayesian models, on the other hand, refrain from specifying a specific prior functional form P, but instead assume a second-level hyperprior on P with support on a suitable space of probability measures ...
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Bayesian learning for neural networks: an algorithmic survey
Artificial Intelligence Review, 2023Alexandros Iosifidis +1 more
exaly
1986
In this paper Bayesian methods are applied to dynamical linear models \[ Y_ t=F_ t\theta_ t+v_ t,\quad \theta_ t=G_ t\theta_{t- 1}+w_ t, \] with normal distribution assumptions and dynamical generalized linear models with observations of the exponential family.
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In this paper Bayesian methods are applied to dynamical linear models \[ Y_ t=F_ t\theta_ t+v_ t,\quad \theta_ t=G_ t\theta_{t- 1}+w_ t, \] with normal distribution assumptions and dynamical generalized linear models with observations of the exponential family.
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