Results 131 to 140 of about 3,990 (166)

Bayesian Estimation of the Number of Species using Noninformative Priors

Biometrical Journal, 2008
AbstractConsider a sample of animal abundances collected from one sampling occasion. Our focus is in estimating the number of species in a closed population. In order to conduct a noninformative Bayesian inference when modeling this data, we derive Jeffreys and reference priors from the full likelihood.
John Bunge
exaly   +3 more sources

Noninformative priors for inferences in exponential regression models

Biometrika, 1991
Reference priors [\textit{J. O. Berger} and \textit{J. M. Bernardo}, J. Am. Stat. Assoc. 84, No. 405, 200-207 (1989; Zbl 0682.62018)] are used to estimate the regression parameter \(\rho\) in the exponential regression model given by \[ Y_{ij}\sim N(\alpha+\beta\rho^{x+x_ ia},\sigma^ 2), \] where \(\alpha,\beta\in R\), \(0 < \rho < 1\), \(x\) and \(a\)
Ye, Keying, Berger, James O.
openaire   +2 more sources

Hidden Dangers of Specifying Noninformative Priors

The American Statistician, 2012
“Noninformative” priors are widely used in Bayesian inference. Diffuse priors are often placed on parameters that are components of some function of interest. That function may, of course, have a prior distribution that is highly informative, in contrast to the joint prior placed on its arguments, resulting in unintended influence on the posterior for ...
John W. Seaman, John W. Seaman, James D. Stamey   +2 more
openaire   +1 more source

A note on noninformative priors for Weibull distributions

Journal of Statistical Planning and Inference, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Constrained noninformative priors in risk assessment

Reliability Engineering and System Safety, 1996
Abstract A constrained noninformative prior distribution, a generalization of the Jeffreys noninformative prior, is defined for a single unknown parameter as the distribution corresponding to the maximum entropy distribution, subject to the assumed constraint(s), in the transformed model where the unknown parameter is approximately a location ...
exaly   +2 more sources

Noninformative priors for the normal variance ratio

Statistical Papers, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, D. H., Kang, S. G., Lee, W. D.
openaire   +2 more sources

Multiresponse parameter estimation with a new and noninformative prior

Biometrika, 1987
Summary: A noninformative prior distribution for the covariance matrix of a normal distribution is derived from a data-relocated likelihood function. This new prior distribution also follows from Jeffreys's rule. Resulting modifications of current formulae for multiresponse parameter estimation are summarized.
openaire   +1 more source

Bayesian analysis of vector-autoregressive models with noninformative priors

Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sun, Dongchu, Ni, Shawn
openaire   +1 more source

Developing Noninformative Priors for Parallel-Line Bioassay [PDF]

Communications for Statistical Applications and Methods, 2002
This paper revisits parallel-line bioassay problem, from a Bayesian point of view using noninformative priors such as Jeffreys' prior, reference priors, and probability matching priors. After finding the orthogonal transformation, the class of first order and second order probability matching priors are derived. Jeffreys' prior and reference priors are
YeongHwa Kim, JungEun Heo
exaly   +2 more sources

Adaptive Bayesian classification using noninformative Dirichlet priors

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166), 2002
A model is developed to illustrate the effect that adapting correctly labeled training data with possibly incorrectly labeled data has on classification performance. The model is based on a previously developed model for mislabeled training data that uses the uniform Dirichlet distribution as a noninformative prior on the symbol probabilities of each ...
Robert S. Lynch Jr., Peter K. Willett
openaire   +1 more source