# Bayesian Inference

## Definition of Bayesian Inference

Bayesian Inference is a statistical method of using prior knowledge and data to generate probabilities about future events.

## What is Bayesian Inference used for?

Bayesian inference is a statistical method used to update existing beliefs based on new, observed data. It is an important tool in the field of machine learning and data science, as it can be used to analyze data and predict outcomes. Bayesian inference works by using prior knowledge of a given problem to determine the probability of certain outcomes. This prior knowledge then combines with current observations to form a posteriorset of beliefs about a given outcome. For example, Bayesian inference can be used to determine how likely it is that a particular medical test will detect a disease given the patient’s symptoms or that an online ad will convert into a purchase based on its placement and target audience.

The two main components of Bayesian inference are Bayes Theorem and priors. Bayes Theorem is an equation for calculating the posterior probability from prior probabilities and observed data. Priors represent prior beliefs about the outcome of an event, which can be either subjective or objective depending on the situation. To create more accurate predictions, more complex models may use multiple factors and information sources to produce more accurate results than those produced by single-factor models.

Bayesian inference is useful in many fields such as medicine, engineering, economics, finance, and analytics due to its ability to incorporate prior information without completely disregarding current evidence. For instance, it can help doctors better diagnose diseases by gathering additional information like family history or environmental factors that weren’t available before running tests; engineers can develop solutions faster by utilizing data collected from past projects; economists can gain better insights into economic trends; finance professionals can make more reliable investments; and analytics experts can develop strategies for improving customer experiences and marketing campaigns based on past performance metrics.