scholarly journals On a family of prior distributions for a class of Bayesian search models

1993 ◽  
Vol 25 (03) ◽  
pp. 714-716
Author(s):  
K. D. Glazebrook
Keyword(s):  
Conjugate Prior ◽  
Prior Distributions ◽  
Search Models ◽  
New Family ◽  
Special Cases ◽  
The Family ◽  
The One ◽  
Two Parameters ◽  
Two Parameter ◽  
Conjugate Prior Distributions

We propose a two-parameter family of conjugate prior distributions for the number of undiscovered objects in a class of Bayesian search models. The family contains the one-parameter Euler and Heine families as special cases. The two parameters may be interpreted respectively as an overall success rate and a rate of depletion of the source of objects. The new family gives enhanced flexibility in modelling.

scholarly journals On a family of prior distributions for a class of Bayesian search models

1993 ◽  
Vol 25 (3) ◽  
pp. 714-716 ◽  
Author(s):  
K. D. Glazebrook
Keyword(s):  
Conjugate Prior ◽  
Prior Distributions ◽  
Search Models ◽  
New Family ◽  
Special Cases ◽  
The Family ◽  
The One ◽  
Two Parameters ◽  
Two Parameter ◽  
Conjugate Prior Distributions

We propose a two-parameter family of conjugate prior distributions for the number of undiscovered objects in a class of Bayesian search models. The family contains the one-parameter Euler and Heine families as special cases. The two parameters may be interpreted respectively as an overall success rate and a rate of depletion of the source of objects. The new family gives enhanced flexibility in modelling.

scholarly journals A channel-based perspective on conjugate priors

2020 ◽  
Vol 30 (1) ◽  
pp. 44-61 ◽  
Author(s):  
B. Jacobs
Keyword(s):  
Statistical Model ◽  
Posterior Distribution ◽  
Prior Distribution ◽  
Closure Property ◽  
Bayesian Probability ◽  
Conjugate Prior ◽  
Prior Distributions ◽  
Graphical Language ◽  
Conjugate Priors ◽  
Abstract Formulation

AbstractA desired closure property in Bayesian probability is that an updated posterior distribution be in the same class of distributions – say Gaussians – as the prior distribution. When the updating takes place via a statistical model, one calls the class of prior distributions the ‘conjugate priors’ of the model. This paper gives (1) an abstract formulation of this notion of conjugate prior, using channels, in a graphical language, (2) a simple abstract proof that such conjugate priors yield Bayesian inversions and (3) an extension to multiple updates. The theory is illustrated with several standard examples.

Localization of Intracellular Antigens in thin Sections with Ferritin Labeled Antibody

1970 ◽  
Vol 28 ◽  
pp. 174-175
Author(s):  
M.S. Shahrabadi ◽  
T. Yamamoto
Keyword(s):  
Bovine Serum Albumin ◽  
Bovine Serum ◽  
Antigenic Determinants ◽  
Conjugate Prior ◽  
Thin Sections ◽  
Specific Attachment ◽  
Intracellular Antigen ◽  
Antigen Antibody ◽  
Ideal Method ◽  
The Ideal

The technique of labeling of macromolecules with ferritin conjugated antibody has been successfully used for extracellular antigen by means of staining the specimen with conjugate prior to fixation and embedding. However, the ideal method to determine the location of intracellular antigen would be to do the antigen-antibody reaction in thin sections. This technique contains inherent problems such as the destruction of antigenic determinants during fixation or embedding and the non-specific attachment of conjugate to the embedding media. Certain embedding media such as polyampholytes (2) or cross-linked bovine serum albumin (3) have been introduced to overcome some of these problems.

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.

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