Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing

1996 ◽  
pp. 206-227 ◽  
Author(s):  
Merlise Clyde ◽  
Giovanni Parmigiani
Keyword(s):  
Prior Distributions ◽  
Model Mixing

scholarly journals BNNpriors: A library for Bayesian neural network inference with different prior distributions

2021 ◽  
pp. 100079
Author(s):  
Vincent Fortuin ◽  
Adrià Garriga-Alonso ◽  
Mark van der Wilk ◽  
Laurence Aitchison
Keyword(s):  
Neural Network ◽  
Network Inference ◽  
Bayesian Neural Network ◽  
Prior Distributions

scholarly journals An FDA-Based Approach for Clustering Elicited Expert Knowledge

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 184-204
Author(s):  
Carlos Barrera-Causil ◽  
Juan Carlos Correa ◽  
Andrew Zamecnik ◽  
Francisco Torres-Avilés ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Functional Data ◽  
Expert Knowledge ◽  
Probability Distributions ◽  
Knowledge Elicitation ◽  
Parallel Analysis ◽  
Data Sets ◽  
Dimensional Problem ◽  
Prior Distributions ◽  
Infinite Dimensional ◽  
Finite Dimensional

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

Worth of perfect information in bernoulli bandits

1991 ◽  
Vol 23 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Donald A. Berry ◽  
Robert P. Kertz
Keyword(s):  
Perfect Information ◽  
Prior Distributions ◽  
Minimax Approach

For k-armed Bernoulli bandits with discounting, sharp comparisons are given between average optimal rewards for a gambler and for a ‘perfectly informed' gambler, over natural collections of prior distributions. Some of these comparisons are proved under general discounting, and others under non-increasing discount sequences. Connections are made between these comparisons and the concept of ‘regret' in the minimax approach to bandit processes. Identification of extremal cases in the sharp comparisons is emphasized.

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