Bayesian-statistics-Online-Journals

 Bayesian statistics is a statistical theory based on the Bayesian interpretation of probability, in which probability expresses a degree of belief in an event. The degree of belief can be based on historical experience of the case, such as the outcomes of previous studies, or personal opinions about it. It differs from a number of other probability theories, such as the frequentist interpretation that considers probability as the limit of the relative occurrence of an event after several trials. Bayesian statistics is a method that uses the mathematical language of probability to explain epistemological incertitude. Degrees of belief in states of nature are specified in the 'Bayesian Paradigm;' these are non-negative, and total belief in all states of nature is fixed as one. Bayesian statistical methods start with existing 'prior' beliefs, and update them using data to give 'posterior' beliefs which can be used as the basis for inferential decisions. Online Journals are journals that are reviewed by scholars and peers. Online Journalism also strives to attract multiple readers worldwide by completing the initial and latest work on the main problems of the above-mentioned disciplines. Journals allow all readers to read, view, download and print out, without subscription or limitations, the full text of all published articles.  

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