Applying Bayes' Theorem in Deduction

View

This is a book resource with multiple pages. Navigate between the pages using the buttons.

Bayes' theorem

One of the common ways to use conditional probability is through Bayes' Theorem. The definition of conditional probability is used in Bayes' Theorem to render inferences in many situations where events are causally linked.

In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately by conditioning it relative to their age, rather than simply assuming that the individual is typical of the population as a whole.

One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics, being considered by one authority as "to the theory of probability what Pythagoras's theorem is to geometry".

Source: Wikipedia, https://en.wikipedia.org/wiki/Bayes%27_theorem
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 License.