Results 281 to 290 of about 2,663,278 (338)

Bayesian multioutput feedforward neural networks comparison: a conjugate prior approach

IEEE Transactions on Neural Networks, 2006
A Bayesian method for the comparison and selection of multioutput feedforward neural network topology, based on the predictive capability, is proposed. As a measure of the prediction fitness potential, an expected utility criterion is considered which is consistently estimated by a sample-reuse computation.
Vivien Rossi, J. Vila
semanticscholar   +5 more sources

Conjugate priors for generalized MaxEnt families

AIP Conference Proceedings, 2014
Bayes theorem can be seen as the result of an optimization problem. By slightly altering this optimization problem many generalized Bayes rules can be constructed. In this work we show that a notion of a conjugate prior for non exponential family distributions can be recovered if one uses one of these generalized rules.
Brendan van Rooyen, Mark D. Reid
openaire   +1 more source

Gibbs sampling, conjugate priors and coupling

Sankhya A, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Diaconis, Persi, Khare, Kshitij, Saloff-Coste, Laurent   +2 more
openaire   +1 more source

The empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the MLE method

Communications in Statistics - Theory and Methods, 2019
Most of the samples in the real world are from the normal distributions with unknown mean and variance, for which it is common to assume a conjugate normal-inverse-gamma prior.
Ying-Ying Zhang, Tengzhong Rong, Man-Man Li   +2 more
semanticscholar   +1 more source

Conjugate priors for exponential-type processes

Statistics & Probability Letters, 1991
Let \(X(t)\), \(t\in T\), be a stochastic process defined on a probability space \((\Omega,{\mathcal F},P_ \theta)\) with values in the measurable space \((R^ k,{\mathcal B}(R^ k))\) where \(T=[0,\infty)\) or \(T=\{0,1,2,\dots\}\) and \(\theta\) is a parameter with values in an open set \(\Theta\subset R^ n\).
Magiera, Ryszard, Wilczyński, Maciej
openaire   +1 more source

Conditionally Reducible Natural Exponential Families and Enriched Conjugate Priors

Scandinavian Journal of Statistics, 2001
Consider a standard conjugate family of prior distributions for a vector‐parameter indexing an exponential family. Two distinct model parameterizations may well lead to standard conjugate families which are not consistent, i.e. one family cannot be derived from the other by the usual change‐of‐variable technique.
G. CONSONNI, VERONESE, PIERO
openaire   +2 more sources

Conjugate Priors for Exponential Families Having Quadratic Variance Functions

Journal of the American Statistical Association, 1992
Abstract Consider a natural exponential family parameterized by θ. It is well known that the standard conjugate prior on θ is characterized by a condition of posterior linearity for the expectation of the model mean parameter μ. Often, however, this family is not parameterized in terms of θ but rather in terms of a more usual parameter, such at the ...
G. Consonni, VERONESE, PIERO
openaire   +1 more source

Recursive Bayesian prediction of remaining useful life for gamma degradation process under conjugate priors

Scandinavian Journal of Statistics
The Gamma process stands as a prevalent model for monotonic degradation data. However, its statistical inference faces complexity due to the intricate parameter structure within the likelihood function. This paper addresses this challenge by deriving a
Ancha Xu, Weiwei Wang
semanticscholar   +1 more source

ANALYSIS OF SEQUENTIAL MONITORING SCHEMES USING NATURAL CONJUGATE PRIORS

International Journal of Reliability, Quality and Safety Engineering, 2010
The complexity of modern manufacturing underscores the need for improved control strategies for these systems. We formulate a Bayesian attribute sequential monitoring plan useful for adaptive control of a production process. The Bayesian framework is based on natural informative conjugate priors that provide an optimal adjustment interval k in response
Okogbaa, OG, Devarakonda, L, Otieno, W, ALBINO, Vito   +3 more
openaire   +2 more sources

Conjugate Exponential Family Priors For Exponential Family Likelihoods

Statistics, 1993
General classes of conjugate exponential family priors are identified for exponential family likelihoods. Both joint and conditional specification of the priors are discussed. The normal and inverse Gaussian cases provide illustrations.
Barry C. Arnold, Enrique Castillo, Jose María Sarabia   +2 more
openaire   +1 more source