scholarly journals CDMBE: A Case Description Model Based on Evidence

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Jianlin Zhu ◽  
Xiaoping Yang ◽  
Jing Zhou
Keyword(s):  
Bayesian Model ◽  
Evidence Based ◽  
Inference Rules ◽  
Case Description ◽  
Criminal Cases ◽  
Law System ◽  
Model Based ◽  
Practical Inference ◽  
Logical Reason ◽  
Argument Map

By combining the advantages of argument map and Bayesian network, a case description model based on evidence (CDMBE), which is suitable to continental law system, is proposed to describe the criminal cases. The logic of the model adopts the credibility logical reason and gets evidence-based reasoning quantitatively based on evidences. In order to consist with practical inference rules, five types of relationship and a set of rules are defined to calculate the credibility of assumptions based on the credibility and supportability of the related evidences. Experiments show that the model can get users’ ideas into a figure and the results calculated from CDMBE are in line with those from Bayesian model.

Training Methods With Clinicians and Students—A Personal Story and Change: Narrative Therapy for Stuttering

2008 ◽  
Vol 18 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Margaret Leahy
Keyword(s):  
Narrative Therapy ◽  
Evidence Based Practice ◽  
Theoretical Background ◽  
Evidence Based ◽  
Training Methods ◽  
Sense Of Control ◽  
Case Description ◽  
General Outline ◽  
Personal Story ◽  
Fluency Disorders

Abstract Educating students and informing clinicians regarding developments in therapy approaches and in evidence-based practice are important elements of the responsibility of specialist academic posts in universities. In this article, the development of narrative therapy and its theoretical background are outlined (preceded by a general outline of how the topic of fluency disorders is introduced to students at an Irish university). An example of implementing narrative therapy with a 12-year-old boy is presented. The brief case description demonstrates how narrative therapy facilitated this 12-year-old make sense of his dysfluency and his phonological disorder, leading to his improved understanding and management of the problems, fostering a sense of control that led ultimately to their resolution.

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.

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 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 Legal Judgment Prediction Based on Multiclass Information Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kongfan Zhu ◽  
Rundong Guo ◽  
Weifeng Hu ◽  
Zeqiang Li ◽  
Yujun Li
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
External Information ◽  
Criminal Cases ◽  
Law System ◽  
Large Scale Dataset ◽  
Assistant Systems ◽  
Civil Law System

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

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

scholarly journals Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 890
Author(s):  
Sergey Oladyshkin ◽  
Farid Mohammadi ◽  
Ilja Kroeker ◽  
Wolfgang Nowak
Keyword(s):  
Bayesian Inference ◽  
Active Learning ◽  
Gaussian Process ◽  
Relative Entropy ◽  
Bayesian Model ◽  
Information Gain ◽  
Measurement Data ◽  
Superior Performance ◽  
Evidence Based ◽  
Model Evidence

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.

Indian Judgment Categorization for Practicing Similar Judgment Identification

2021 ◽  
pp. 232-241
Author(s):  
Jenish Dhanani ◽  
Rupa G. Mehta ◽  
Dipti P. Rana ◽  
Rahul Lad ◽  
Amogh Agrawal ◽  
...  
Keyword(s):  
Search Space ◽  
Automated System ◽  
Court Case ◽  
Criminal Cases ◽  
Legal Information ◽  
Law System ◽  
Legal Professionals ◽  
Related Information ◽  
Legal Document ◽  
Legal Domain

Recently, legal information retrieval has emerged as an essential practice for the legal fraternity. In the legal domain, judgment is a specific kind of legal document, which discusses case-related information and the verdict of a court case. In the common law system, the legal professionals exploit relevant judgments to prepare arguments. Hence, an automated system is a vital demand to identify similar judgments effectively. The judgments can be broadly categorized into civil and criminal cases, where judgments with similar case matters can have strong relevance compared to judgments with different case matters. In similar judgment identification, categorized judgments can significantly prune search space by restrictive search within a specific case category. So, this chapter provides a novel methodology that classifies Indian judgments in either of the case matter. Crucial challenges like imbalance and intrinsic characteristics of legal data are also highlighted specific to similarity analysis of Indian judgments, which can be a motivating aspect to the research community.

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