scholarly journals Belief Merging Operators as Maximum Likelihood Estimators

2020 ◽  
Author(s):  
Patricia Everaere ◽  
Sebastien Konieczny ◽  
Pierre Marquis
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimators ◽  
Noise Model ◽  
Belief Merging ◽  
The World ◽  
Natural Noise ◽  
True State ◽  
Merging Process ◽  
Rationality Postulates ◽  
Noise Models

We study how belief merging operators can be considered as maximum likelihood estimators, i.e., we assume that there exists a (unknown) true state of the world and that each agent participating in the merging process receives a noisy signal of it, characterized by a noise model. The objective is then to aggregate the agents' belief bases to make the best possible guess about the true state of the world. In this paper, some logical connections between the rationality postulates for belief merging (IC postulates) and simple conditions over the noise model under consideration are exhibited. These results provide a new justification for IC merging postulates. We also provide results for two specific natural noise models: the world swap noise and the atom swap noise, by identifying distance-based merging operators that are maximum likelihood estimators for these two noise models.

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

2018 ◽  
pp. 397
Author(s):  
Nadia Hashim Al-Noor ◽  
Shurooq A.K. Al-Sultany
Keyword(s):  
Maximum Likelihood ◽  
Expectation Maximization ◽  
Mean Squared Error ◽  
Maximum Likelihood Estimators ◽  
Reliability Function ◽  
Fuzzy Data ◽  
Monte Carlo Simulation Study ◽  
Weibull Parameters ◽  
Squared Error ◽  
Newton Raphson

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

The identification of a novel HIV-1 second-generation recombinant form (CRF01_AE/CRF07_BC) among men who have sex with men in Jiangsu, China

2020 ◽  
Vol 18 ◽  
Author(s):  
Yin Yueqi ◽  
Zhou Ying ◽  
Lu Jing ◽  
Guo Hongxiong ◽  
Chen Jianshuang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Trees ◽  
Second Generation ◽  
Emergency Situation ◽  
The World ◽  
Break Points ◽  
Sex With Men ◽  
Full Length Genome ◽  
Virus Strains ◽  
Hiv 1

Background: CRF01_AE and CRF07_BC are the two major HIV-1 virus strains circulating in China. The proportion of dominant subtypes (CRF01_AE and CRF07_BC) among MSM in Jiangsu province was over 80%. A large number of URFs have been found in China in recently years. Objective: This study aimed to report on novel HIV-1 recombinants. Method: We constructed Phylogenetic trees using the maximum likelihood (ML) method with 1000 bootstrap replicates in IQ-TREE 1.6.8 software and determined recombination break points using SimPlot 3.5.1. Results: We identified a novel, second-generation HIV-1 recombinant (JS020202) between CRF01_AE and CRF07_BC. The analysis of near full-length genome (NFLG) showed there were at least 8 breakpoints inner virus, which differed from any previously identified CRF and URF around the world. Conclusion: Novel diverse CRF01_AE/07_BC suggested the complexity trends of HIV-1 genetics. The emergency situation of diverse recombinant strains should be monitored continuously.

Inference on P(Y

2020 ◽  
Vol 72 (2) ◽  
pp. 89-110
Author(s):  
Manoj Chacko ◽  
Shiny Mathew
Keyword(s):  
Maximum Likelihood ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Likelihood Estimators ◽  
Record Values ◽  
Asymptotic Distributions ◽  
Bayes Estimators ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this article, the estimation of [Formula: see text] is considered when [Formula: see text] and [Formula: see text] are two independent generalized Pareto distributions. The maximum likelihood estimators and Bayes estimators of [Formula: see text] are obtained based on record values. The Asymptotic distributions are also obtained together with the corresponding confidence interval of [Formula: see text]. AMS 2000 subject classification: 90B25

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