the weibull distribution has been widely used in survival and engineering reliability analysis. in life testing experiments is fairly common practice to terminate the experiment before all the items have failed, that means the data are censored. thus, the main objective of this paper is to estimate the reliability function of the weibull distribution with uncensored and censored data by using b...

In the preceding paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem of selecting the model which is most probable in view of the data and all the prior information. In addition to the analytic calculation, two examples are given. The rst example demonstrates how to use Bayesian probability theory...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

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Bayesian Data Analysis. Bayesian inference is too narrow; Bayesian statistics is too broad. Bayes is a good brand name; Statistics using conditional. Bayesian Data Analysis: Straightline fitting. Stephen F. Gull. Cavendish Laboratory,. Madingley Road,. Cambridge CB3 OHE, U.K Abstract. A Bayesian Overview. Bayesian data analysis. John K. Kruschke. . Bayesian methods have garnered huge interest i...

Probability theory, when interpreted as logic, enables one to ask many questions not possible with the frequency interpretation of probability theory. Often, answering these questions can be computationally intensive. If these techniques are to nd their way i n to general use in NMR, a way that allows one to calculate the probability for the frequencies, amplitudes, and decay rate constants qui...

A Bayesian analysis concerns the quantification and updating of knowledge according to the laws of probability theory. A Bayesian analysis allows researchers to estimate parameters (e.g., how effective is a new treatment?) or to conduct hypothesis testing and model selection (e.g., is the new treatment more effective than the standard treatment?). The Bayesian paradigm offers concrete advantage...