scholarly journals Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood

2021 ◽  
Author(s):  
Dmytro Perepolkin ◽  
Benjamin Goodrich ◽  
Ullrika Sahlin
Keyword(s):  
Bayesian Inference ◽  
Bayesian Model ◽  
Probability Distributions ◽  
Expert Elicitation ◽  
Model Based ◽  
Dirichlet Prior

This paper extends the application of indirect Bayesian inference to probability distributions defined in terms of quantiles of the observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and interpretability of its parameters, and are therefore useful for elicitation on observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a version of the Dirichlet prior. The resulting “hybrid” expert elicitation protocol for characterizing uncertainty in parameters using questions about the observable quantities is discussed and contrasted to parametric and predictive elicitation.

Combining deep learning and model-based method using Bayesian Inference for walking speed estimation

2020 ◽  
Vol 62 ◽  
pp. 102117
Author(s):  
Yuyang Qian ◽  
Kaiming Yang ◽  
Yu Zhu ◽  
Wei Wang ◽  
Chenhui Wan
Keyword(s):  
Deep Learning ◽  
Bayesian Inference ◽  
Walking Speed ◽  
Speed Estimation ◽  
Model Based

scholarly journals A Bayesian Account of Memory for Text

2019 ◽  
Author(s):  
Mark Andrews
Keyword(s):  
Bayesian Inference ◽  
Strong Evidence ◽  
Bayesian Model ◽  
Background Knowledge ◽  
Predictive Inference ◽  
Memory Representation ◽  
British English ◽  
Text Memory ◽  
Memory Experiment ◽  
Memory For Text

The study of memory for texts has had an long tradition of research in psychology. According to most general accounts, the recognition or recall of items in a text is based on querying a memory representation that is built up on the basis of background knowledge. The objective of this paper is to describe and thoroughly test a Bayesian model of these general accounts. In particular, we present a model that describes how we use our background knowledge to form memories in terms of Bayesian inference of statistical patterns in the text, followed by posterior predictive inference of the words that are typical of those inferred patterns. This provides us with precise predictions about which words will be remembered, whether veridically or erroneously, from any given text. We tested these predictions using behavioural data from a memory experiment using a large sample of randomly chosen texts from a representative corpus of British English. The results show that the probability of remembering any given word in the text, whether falsely or veridically, is well predicted by the Bayesian model. Moreover, compared to nontrivial alternative models of text memory, by every measure used in the analyses, the predictions of the Bayesian model were superior, often overwhelmingly so. We conclude that these results provide strong evidence in favour of the Bayesian account of text memory that we have presented in this paper.

A Hierarchical Bayesian Model-Based Uncertainty Analysis for Tsunami Heights along Shorelines in Korea

2016 ◽  
Vol 75 (sp1) ◽  
pp. 1157-1161 ◽  
Author(s):  
Hyun-Han Kwon ◽  
Jin-Young Kim ◽  
Byoung Han Choi ◽  
Yong-Sik Cho
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Model Based

Continual Learning with Bayesian Model Based on a Fixed Pre-trained Feature Extractor

2021 ◽  
pp. 397-406
Author(s):  
Yang Yang ◽  
Zhiying Cui ◽  
Junjie Xu ◽  
Changhong Zhong ◽  
Ruixuan Wang ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Model Based ◽  
Feature Extractor ◽  
Continual Learning

scholarly journals Scenario Analysis of Initial Water-Rights Allocation to Improve Regional Water Productivities

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1312 ◽  
Author(s):  
Tienan Li ◽  
Xueting Zeng ◽  
Cong Chen ◽  
Xiangmin Kong ◽  
Junlong Zhang ◽  
...  
Keyword(s):  
Water Policy ◽  
Water Resource Management ◽  
Probability Distributions ◽  
Water Productivity ◽  
Water Rights ◽  
Risk Attitude ◽  
Risk Attitudes ◽  
Practical Case ◽  
Initial Water ◽  
Model Based

In this study, an initial water-rights allocation (IWRA) model is proposed for adjusting the traditional initial water-rights empowerment model based on previous water intake permits, with the aim of improving the productivity of water resources under population growth and economic development. A stochastic scenario with Laplace criterion mixed fuzzy programming (SSLF) is developed into an IWRA model to deal with multiple uncertainties and complexities, which includes dynamic water demand, changing water policy, adjusted tradable water rights, the precise risk attitude of policymakers, development of the economy, and their interactions. SSLF not only deals with fuzziness in probability distributions with high satisfaction degrees, but also reflects the risk attitudes of policymakers with the Laplace criterion, which can handle the probability of scenario occurrence under the supposition of no data available. The developed IWRA model with the SSLF method is applied to a practical case in an alpine region of China. The results of adjusted initial water rights, optimal water-right allocation, changed industrial structure, and system benefits under various scenarios associated with risk attitudes and water productivity improvement were obtained and analyzed. It was found that the current initial water-rights allocation scheme based on previous intake water permits is not efficient, and this can be modified by the IWRA model. Based on the strategies of drinking safety and ecological security, the main tradeoff between agricultural and industrial water rights can facilitate optimization of the current initial water-rights allocation. This can assist policymakers in producing an effective plan to promote water productivity and water resource management in a robust and reliable manner.

scholarly journals A Bayesian model integration for mutation calling through data partitioning

2019 ◽  
Vol 35 (21) ◽  
pp. 4247-4254 ◽  
Author(s):  
Takuya Moriyama ◽  
Seiya Imoto ◽  
Shuto Hayashi ◽  
Yuichi Shiraishi ◽  
Satoru Miyano ◽  
...  
Keyword(s):  
Hierarchical Model ◽  
Bayesian Model ◽  
Information Sources ◽  
Tumor Model ◽  
Bayesian Hierarchical Model ◽  
Generative Models ◽  
Error Model ◽  
Model Integration ◽  
Bayesian Hierarchical ◽  
Model Based

Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hierarchical Bayesian model to inferPL(Z)relations usingGaiaparallaxes

2019 ◽  
Vol 623 ◽  
pp. A156 ◽  
Author(s):  
H. E. Delgado ◽  
L. M. Sarro ◽  
G. Clementini ◽  
T. Muraveva ◽  
A. Garofalo
Keyword(s):  
Bayesian Model ◽  
Probability Distributions ◽  
Synthetic Data ◽  
Full Description ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Data Release ◽  
Lyrae Stars

In a recent study we analysed period–luminosity–metallicity (PLZ) relations for RR Lyrae stars using theGaiaData Release 2 (DR2) parallaxes. It built on a previous work that was based on the firstGaiaData Release (DR1), and also included period–luminosity (PL) relations for Cepheids and RR Lyrae stars. The method used to infer the relations fromGaiaDR2 data and one of the methods used forGaiaDR1 data was based on a Bayesian model, the full description of which was deferred to a subsequent publication. This paper presents the Bayesian method for the inference of the parameters ofPL(Z) relations used in those studies, the main feature of which is to manage the uncertainties on observables in a rigorous and well-founded way. The method encodes the probability relationships between the variables of the problem in a hierarchical Bayesian model and infers the posterior probability distributions of thePL(Z) relationship coefficients using Markov chain Monte Carlo simulation techniques. We evaluate the method with several semi-synthetic data sets and apply it to a sample of 200 fundamental and first-overtone RR Lyrae stars for whichGaiaDR1 parallaxes and literatureKs-band mean magnitudes are available. We define and test several hyperprior probabilities to verify their adequacy and check the sensitivity of the solution with respect to the prior choice. The main conclusion of this work, based on the test with semi-syntheticGaiaDR1 parallaxes, is the absolute necessity of incorporating the existing correlations between the period, metallicity, and parallax measurements in the form of model priors in order to avoid systematically biased results, especially in the case of non-negligible uncertainties in the parallaxes. The relation coefficients obtained here have been superseded by those presented in our recent paper that incorporates the findings of this work and the more recentGaiaDR2 measurements.

Hierarchical Bayesian Model Based on Robust Fixed Rank Filter for Fusing MODIS SST and AMSR-E SST

2019 ◽  
Vol 85 (2) ◽  
pp. 119-131 ◽  
Author(s):  
Yuxin Zhu ◽  
Emily Lei Kang ◽  
Yanchen Bo ◽  
Jinzong Zhang ◽  
Yuexiang Wang ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Model Based ◽  
Fixed Rank

Optimal Nonparametric Bayesian Model-Based Multimodal BoVW Creation Using Multilayer pLSA

2019 ◽  
Vol 39 (2) ◽  
pp. 1123-1132 ◽  
Author(s):  
G. Nagarajan ◽  
R. I. Minu ◽  
A. Jayanthila Devi
Keyword(s):  
Bayesian Model ◽  
Nonparametric Bayesian ◽  
Model Based
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