scholarly journals Bayesian Inference for Skew-Symmetric Distributions

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 491
Author(s):  
Fatemeh Ghaderinezhad ◽  
Christophe Ley ◽  
Nicola Loperfido
Keyword(s):  
Bayesian Inference ◽  
Bayesian Approach ◽  
Symmetric Distributions ◽  
Prior Distributions ◽  
Univariate Case ◽  
Frequentist Inference ◽  
Skew Normal

Skew-symmetric distributions are a popular family of flexible distributions that conveniently model non-normal features such as skewness, kurtosis and multimodality. Unfortunately, their frequentist inference poses several difficulties, which may be adequately addressed by means of a Bayesian approach. This paper reviews the main prior distributions proposed for the parameters of skew-symmetric distributions, with special emphasis on the skew-normal and the skew-t distributions which are the most prominent skew-symmetric models. The paper focuses on the univariate case in the absence of covariates, but more general models are also discussed.

scholarly journals PathFinder: Bayesian inference of clone migration histories in cancer

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i675-i683
Author(s):  
Sudhir Kumar ◽  
Antonia Chroni ◽  
Koichiro Tamura ◽  
Maxwell Sanderford ◽  
Olumide Oladeinde ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Cancer Cells ◽  
Bayesian Approach ◽  
Genetic Variants ◽  
Cancer Development ◽  
Posterior Probabilities ◽  
Migration Routes ◽  
Primary Tumors ◽  
History Of ◽  
The Web

Abstract Summary Metastases cause a vast majority of cancer morbidity and mortality. Metastatic clones are formed by dispersal of cancer cells to secondary tissues, and are not medically detected or visible until later stages of cancer development. Clone phylogenies within patients provide a means of tracing the otherwise inaccessible dynamic history of migrations of cancer cells. Here, we present a new Bayesian approach, PathFinder, for reconstructing the routes of cancer cell migrations. PathFinder uses the clone phylogeny, the number of mutational differences among clones, and the information on the presence and absence of observed clones in primary and metastatic tumors. By analyzing simulated datasets, we found that PathFinder performes well in reconstructing clone migrations from the primary tumor to new metastases as well as between metastases. It was more challenging to trace migrations from metastases back to primary tumors. We found that a vast majority of errors can be corrected by sampling more clones per tumor, and by increasing the number of genetic variants assayed per clone. We also identified situations in which phylogenetic approaches alone are not sufficient to reconstruct migration routes. In conclusion, we anticipate that the use of PathFinder will enable a more reliable inference of migration histories and their posterior probabilities, which is required to assess the relative preponderance of seeding of new metastasis by clones from primary tumors and/or existing metastases. Availability and implementation PathFinder is available on the web at https://github.com/SayakaMiura/PathFinder.

scholarly journals Bayesian Inference for the Difference of Two Proportion Parameters in Over-Reported Two-Sample Binomial Data Using the Doubly Sample

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Dewi Rahardja
Keyword(s):  
Bayesian Inference ◽  
Closed Form ◽  
Bayesian Approach ◽  
Interval Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Posterior Distributions ◽  
Binomial Data ◽  
The Difference ◽  
Data Subject

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

scholarly journals Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions

Biostatistics ◽  
2010 ◽  
Vol 11 (2) ◽  
pp. 317-336 ◽  
Author(s):  
Sylvia Frühwirth-Schnatter ◽  
Saumyadipta Pyne
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Random Effects ◽  
Data Augmentation ◽  
Heterogeneous Data ◽  
High Dimensional ◽  
Finite Mixtures ◽  
Truncated Normal ◽  
T Distribution ◽  
Skew Normal

Abstract Skew-normal and skew-t distributions have proved to be useful for capturing skewness and kurtosis in data directly without transformation. Recently, finite mixtures of such distributions have been considered as a more general tool for handling heterogeneous data involving asymmetric behaviors across subpopulations. We consider such mixture models for both univariate as well as multivariate data. This allows robust modeling of high-dimensional multimodal and asymmetric data generated by popular biotechnological platforms such as flow cytometry. We develop Bayesian inference based on data augmentation and Markov chain Monte Carlo (MCMC) sampling. In addition to the latent allocations, data augmentation is based on a stochastic representation of the skew-normal distribution in terms of a random-effects model with truncated normal random effects. For finite mixtures of skew normals, this leads to a Gibbs sampling scheme that draws from standard densities only. This MCMC scheme is extended to mixtures of skew-t distributions based on representing the skew-t distribution as a scale mixture of skew normals. As an important application of our new method, we demonstrate how it provides a new computational framework for automated analysis of high-dimensional flow cytometric data. Using multivariate skew-normal and skew-t mixture models, we could model non-Gaussian cell populations rigorously and directly without transformation or projection to lower dimensions.

Bayesian Hypothesis Testing for Hospitality and Tourism Research

2020 ◽  
pp. 109634802094732
Author(s):  
A. George Assaf ◽  
Mike Tsionas
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Approach ◽  
Prior Distributions ◽  
P Values ◽  
Tourism Research ◽  
Bayesian Hypothesis Testing ◽  
Main Challenge ◽  
Hospitality And Tourism ◽  
Practical Recommendations ◽  
The Bayesian Approach

In hospitality and tourism research, p-values continue to be the most common approach to hypothesis testing. In this article, we elaborate on some of the misconceptions associated with p-values. We discuss the advantages of the Bayesian approach and provide several important practical recommendations and considerations for Bayesian hypothesis testing. With the main challenge of Bayesian hypothesis testing being the sensitivity of the results to prior distributions, we present in this article several priors that can be used for that purpose and illustrate their performance in a regression context.

scholarly journals Bayesian inference for a skew-normal IRT model under the centred parameterization

2011 ◽  
Vol 55 (1) ◽  
pp. 353-365 ◽  
Author(s):  
Caio L.N. Azevedo ◽  
Heleno Bolfarine ◽  
Dalton F. Andrade
Keyword(s):  
Bayesian Inference ◽  
Irt Model ◽  
Skew Normal

scholarly journals Bayesian approach to landing page testing

2019 ◽  
Author(s):  
Ya. S. Bondarenko ◽  
S. V. Kravchenko
Keyword(s):  
Bayesian Inference ◽  
Loss Function ◽  
Bayesian Approach ◽  
Conversion Rate ◽  
Expected Value ◽  
Testing Methodology ◽  
Rate Optimization

In this paper a Bayesian inference to conversion rate optimization is considered. Bayesian A/B/C testing methodology with the expected value of the loss function computed analytically is proposed. Bayesian A/B/C testing results are presented graphically and descriptively.

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