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Bayes' Theorem - Essay Example

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The theorem provides understanding about the how the probability of a theorem is affected by a new set of evidence. It is applied to in a variety of context to explore a relationship between theory and evidence…
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Bayes Theorem
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Download file to see previous pages Simon Jackman (2009) defines Bayes’ theorem as ‘a theorem that illustrates conditional probability of the set on the given observed outcome, that is obtained from the knowledge of the probability and its outcome (Jackman, 2009)’.
The rules of Bayes’ theorem are based on the basic axioms of probability or conditional probability. It expresses subjective depress of beliefs explaining the repletion through Bayesian statistic fundamental. The mathematical representation of theorem is as follow:
Bayesian statistical method provides in depth understanding about the events. The application of theorem is wide in various fields and subjects, such as, science, biology, mathematics, finance etc. The model is applied to determine relation between the events. In the field of finance the Bayesian method is adopted for financial forecasting. One of the major advantages of Bayesian theorem is the consideration that is given to the previous information. The fact is that many statisticians would disregard previous information in order to prove the objectivity of the current statistics. The Bayesian theorem proves objectivity of the statistics by combining both the sets of information. A very significant advantage noted for the usage of Bayesian theorem is that it provides direct probability statement. This is considerably the best way to interpret confidence interval. On comparison, one can easily find out that frequents statistics would imply a number of tools. On the other hand, Bayesian approach makes use of only one theorem i.e. Bayesian. The fact remains as that Bayesian approach can be used in different situations where most of the tools of frequentist statistics fall short.
In the Bayesian theorem, the conditional probability occur on the bases of unconditional probabilities that are derived using a multiplication rules, that is (Prior x Likelihood) that are further divided by the sum of the possible parameters. Posterior in Bayesian theorem can be identified ...Download file to see next pagesRead More
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