scholarly journals Improper Priors and Improper Posteriors

2021 ◽  
Author(s):  
Gunnar Taraldsen ◽  
Jarle Tufto ◽  
Bo H. Lindqvist
Keyword(s):  
Improper Priors

Improper and proper posteriors with improper priors in a Poisson-gamma hierarchical model

Test ◽  
1999 ◽  
Vol 8 (1) ◽  
pp. 147-166 ◽  
Author(s):  
Petros Hadjicostas ◽  
Scott M. Berry
Keyword(s):  
Hierarchical Model ◽  
Improper Priors

scholarly journals A Flexible Skewed Link Model for Ordinal Outcomes: An Application to Infertility

2020 ◽  
Vol 8 (A) ◽  
pp. 119-124
Author(s):  
Mohammad Chehrazi ◽  
Seyed Hassan Saadat ◽  
Mahmoud Hajiahmadi ◽  
Mirko Spiroski
Keyword(s):  
Monte Carlo ◽  
Ordinal Data ◽  
Monte Carlo Algorithm ◽  
Link Function ◽  
Posterior Distributions ◽  
Response Data ◽  
Improper Priors ◽  
Ordinal Regression Model ◽  
Link Model ◽  
Categorical Response

BACKGROUND: An important issue in modeling categorical response data is the choice of the links. The commonly used complementary log-log link is inclined to link misspecification due to its positive and fixed skewness parameter. AIM: The objective of this paper is to introduce a flexible skewed link function for modeling ordinal data with some covariates. METHODS: We introduce a flexible skewed link model for the cumulative ordinal regression model based on Chen model. RESULTS: The main advantage suggested by the proposed links is the skewed link provide much more identifiable than the existing skewed links. The propriety of posterior distributions under proper and improper priors is explored in detail. An efficient Markov chain Monte Carlo algorithm is developed for sampling from the posterior distribution. CONCLUSION: The proposed methodology is motivated and illustrated by ovary hyperstimulation syndrome data.

scholarly journals Coherent Inference from Improper Priors and from Finitely Additive Priors

1989 ◽  
Vol 17 (2) ◽  
pp. 907-919 ◽  
Author(s):  
David Heath ◽  
William Sudderth
Keyword(s):  
Improper Priors

scholarly journals Approximation of improper priors

Bernoulli ◽  
2016 ◽  
Vol 22 (3) ◽  
pp. 1709-1728 ◽  
Author(s):  
Christele Bioche ◽  
Pierre Druilhet
Keyword(s):  
Improper Priors

scholarly journals A Markov chain representation of the multiple testing problem

2016 ◽  
Vol 27 (2) ◽  
pp. 364-383 ◽  
Author(s):  
Stefano Cabras
Keyword(s):  
Markov Chain ◽  
Multiple Testing ◽  
Bayes Factor ◽  
Alternative Hypothesis ◽  
Multiple Hypothesis Testing ◽  
Bayes Factors ◽  
Rna Seq ◽  
Markov Transition ◽  
Improper Priors ◽  
Alternative Hypotheses

The problem of multiple hypothesis testing can be represented as a Markov process where a new alternative hypothesis is accepted in accordance with its relative evidence to the currently accepted one. This virtual and not formally observed process provides the most probable set of non null hypotheses given the data; it plays the same role as Markov Chain Monte Carlo in approximating a posterior distribution. To apply this representation and obtain the posterior probabilities over all alternative hypotheses, it is enough to have, for each test, barely defined Bayes Factors, e.g. Bayes Factors obtained up to an unknown constant. Such Bayes Factors may either arise from using default and improper priors or from calibrating p-values with respect to their corresponding Bayes Factor lower bound. Both sources of evidence are used to form a Markov transition kernel on the space of hypotheses. The approach leads to easy interpretable results and involves very simple formulas suitable to analyze large datasets as those arising from gene expression data (microarray or RNA-seq experiments).

scholarly journals Coherent Inferences and Improper Priors

1994 ◽  
Vol 22 (3) ◽  
pp. 1177-1194 ◽  
Author(s):  
Patrizia Berti ◽  
Pietro Rigo
Keyword(s):  
Improper Priors

REVISING BELIEFS IN NONIDENTIFIED MODELS

1998 ◽  
Vol 14 (4) ◽  
pp. 483-509 ◽  
Author(s):  
Dale J. Poirier
Keyword(s):  
Bayesian Analysis ◽  
Posterior Distributions ◽  
Free Lunch ◽  
Improper Priors

A Bayesian analysis of a nonidentified model is always possible if a proper prior on all the parameters is specified. There is, however, no Bayesian free lunch. The “price” is that there exist quantities about which the data are uninformative, i.e., their marginal prior and posterior distributions are identical. In the case of improper priors the analysis is problematic—resulting posteriors can be improper. This study investigates both proper and improper cases through a series of examples.

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