scholarly journals Partial information framework: Model-based aggregation of estimates from diverse information sources

2017 ◽  
Vol 11 (2) ◽  
pp. 3781-3814 ◽  
Author(s):  
Ville A. Satopää ◽  
Shane T. Jensen ◽  
Robin Pemantle ◽  
Lyle H. Ungar
Keyword(s):  
Information Sources ◽  
Partial Information ◽  
Model Based ◽  
Framework Model ◽  
Information Framework

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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 Bayesian aggregation of two forecasts in the partial information framework

2016 ◽  
Vol 119 ◽  
pp. 170-180 ◽  
Author(s):  
Philip Ernst ◽  
Robin Pemantle ◽  
Ville Satopää ◽  
Lyle Ungar
Keyword(s):  
Partial Information ◽  
Information Framework ◽  
Bayesian Aggregation

A software service framework model based on agent

2002 ◽  
Vol 7 (1) ◽  
pp. 20-26 ◽  
Author(s):  
He Yan-xiang ◽  
Li Xu-hui ◽  
Lu Hui ◽  
Zhu Xiao-feng
Keyword(s):  
Model Based ◽  
Framework Model ◽  
Software Service ◽  
Service Framework

Implementation and Design of Basketball Technical Action Based on B/S Framework Data Mining System

2013 ◽  
Vol 380-384 ◽  
pp. 2736-2740
Author(s):  
Yun Fei Qin ◽  
Yi Wang
Keyword(s):  
Data Mining ◽  
Basketball Players ◽  
Association Index ◽  
Mining System ◽  
Framework Model ◽  
Data Mining System ◽  
Back Angle ◽  
Information Framework ◽  
The Membership Function ◽  
Basketball Shooting

Basketball is a technical sport, and players' shooting action will affect the basketball hit rate, which affects the players' performance and competition results. Therefore, in the process of usual training, players should strengthen the basketball technology actions, under this background, the shooting action of basketball players are optimized. The basketball players are shooting, body lean back angle, release angle and release initial velocities as research object, the use of the B/S frame data mining system develops basketball shooting skills and competition information framework model, and the use of eight quantile statistics method determines the membership function, establishing the association of basketball technology actions and marks, according to the association index, it can successfully optimize design of the basketball shooting parameters.

Sign in / Sign up

Export Citation Format

Share Document