Extended Bayesian Inference Method for Evaluating Pipe Failure Probability in Corrosion Rate Fluctuation Model

2008 ◽  
Vol 57 (4) ◽  
pp. 401-407 ◽  
Author(s):  
Satoshi OKAJIMA ◽  
Satoshi IZUMI ◽  
Shinsuke SAKAI
Keyword(s):  
Bayesian Inference ◽  
Corrosion Rate ◽  
Failure Probability ◽  
Inference Method ◽  
Pipe Failure ◽  
Rate Fluctuation ◽  
Bayesian Inference Method ◽  
Fluctuation Model

Bayesian Inference Method for Failure Probability of Wall Thinning Pipe in Corrosion Rate Fluctuation Model

2008 ◽  
Author(s):  
Satoshi Okajima ◽  
Satoshi Izumi ◽  
Shinsuke Sakai
Keyword(s):  
Corrosion Rate ◽  
Failure Probability ◽  
Error Term ◽  
Correction Term ◽  
Operational Environment ◽  
Bayes Method ◽  
Wall Thinning ◽  
Rate Fluctuation ◽  
The One ◽  
Linear Bayes

To rationalize the inspection interval for the wall-thinning piping element, the linear-Bayes method was proposed in the previous paper. To derive the simple formula, the linear-Bayes method ignores the corrosion rate change against time. However, this change may be caused by the one of the operational environment. Therefore, without the sufficient monitoring of the environment, the linear-Bayes method may underestimate the failure probability. In this paper, the linear-Bayes method is extended for the wall-thinning model with the corrosion rate fluctuation, which imitates the unexpected change of the corrosion rate. The extension is carried out through following two approaches: the “correction-term” and the “error-term” approaches. The correction-term approach can evaluate the change of corrosion rate, however, it requires sufficient number of inspections. The error-term approach evaluates the failure probability conservatively.

scholarly journals A Bayesian Inference Method Using Monte Carlo Sampling for Estimating the Number of Communities in Bipartite Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-Zheng Wang ◽  
Li Xiong ◽  
Hu-Chen Liu
Keyword(s):  
Monte Carlo ◽  
Bayesian Inference ◽  
Probability Distribution ◽  
Community Detection ◽  
Degree Distribution ◽  
Prior Probability ◽  
Bipartite Networks ◽  
Inference Method ◽  
Prior Probability Distribution ◽  
Bayesian Inference Method

Community detection is an important analysis task for complex networks, including bipartite networks, which consist of nodes of two types and edges connecting only nodes of different types. Many community detection methods take the number of communities in the networks as a fixed known quantity; however, it is impossible to give such information in advance in real-world networks. In our paper, we propose a projection-free Bayesian inference method to determine the number of pure-type communities in bipartite networks. This paper makes the following contributions: (1) we present the first principle derivation of a practical method, using the degree-corrected bipartite stochastic block model that is able to deal with networks with broad degree distributions, for estimating the number of pure-type communities of bipartite networks; (2) a prior probability distribution is proposed over the partition of a bipartite network; (3) we design a Monte Carlo algorithm incorporated with our proposed method and prior probability distribution. We give a demonstration of our algorithm on synthetic bipartite networks including an easy case with a homogeneous degree distribution and a difficult case with a heterogeneous degree distribution. The results show that the algorithm gives the correct number of communities of synthetic networks in most cases and outperforms the projection method especially in the networks with heterogeneous degree distributions.

Bayesian Inference of Information Theoretic Metrics of Anonymity

2014 ◽  
Vol 989-994 ◽  
pp. 4680-4683
Author(s):  
Han Ru Pei ◽  
Zhi Jian Wang ◽  
Yu Wang
Keyword(s):  
Bayesian Inference ◽  
Probability Distribution ◽  
Inference Method ◽  
Information Theoretic ◽  
System Parameters ◽  
Bayesian Inference Method

Information theoretic metrics is popular theory to measure anonymity. However the difficulty in getting the probability distribution of subjects hampers its practical usage. In this paper we propose a Bayesian inference method to tackle this problem. Our method makes it possible to compare the anonymity of different anonymous systems. We use this method to analyze Threshold Mix and point out different system parameters which do and do not have influence on anonymity.

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