1. Deriving Bayes' Theorem Bayes' theorem is a piece of mathematics. It is called a theorem because it is derivable from a simple definition in probability theory. As a piece of mathematics, it is not controversial. Bayesianism, on the other hand, is a controversial philosophical theory in epistemology. It proposes that the mathematics of probability theory can be put to work in explicating var...

Abstract The spatial variability of the magnitude–frequency distribution is important to improve earthquake forecasting capabilities at different time scales. Here, we develop a novel approach, based on weighted maximum-likelihood estimation, build model for b-value parameter Gutenberg–Richter law and its uncertainty, also catalogs with time-varying completeness magnitude. Then, provide guideli...

Goldstein and Shaw (2002) developed Bayes linear kinematics describing how a Bayes linear analysis should be carried out when we only receive partial information which changes our beliefs about a vector of random quantities in some generalised way. In this paper we explore principles of local computation for Bayes linear kinematic updates. This theory is illustrated by the Bayes linear Bayes mo...

to compare different qtl mapping methods, a population with genotypic and phenotypic data was simulated. in bayesian approach, all information of markers can be used along with combination of distributions of snp markers. it is assumed that most of the markers (95%) have minor effects and a few numbers of markers (5%) exert major effects. the simulated population included a basic population of ...

This article examines the consistency, interpretation and application of Bayes factors constructed from standard test statistics. Primary conclusions are that Bayes factors based on multinomial and normal test statistics are consistent for suitable choices of the hyperparameters used to specify alternative hypotheses, and that such constructions can be extended to obtain consistent Bayes factor...

This paper presents our submission to SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Naïve Bayes Multinomial with standard text-representation features, we approached Subtask B as a seq...

This paper focuses on a Bayes inference model for a simple step-stress life test using Type-I censored sample in a discrete set-up. Assuming the failure times at each stress level are geometrically distributed, the Bayes estimation problem of the parameters of interest is investigated in the both of point and interval approaches. To derive the Bayesian point estimators, some various balanced lo...

It is well-known that Naive Bayes can only represent linearly separable functions in binary domains. But the learnability of general Augmented Naive Bayes is open. Little work is done on the learnability of Bayesian networks in nominal domains, a general case of binary domains. This paper explores the learnability of Augmented Naive Bayes in nominal domains. We introduce a complexity measure fo...

A goal that I have set for myself as Editor is to add some new features to our standard fare of erudite technical articles. There are always times when those who do science need to pause and reflect about the broader implications of what they do. It occurred to me that an occasional column by writers who want to probe into such matters might interest a large cross-section of our readers. Drs. J...