BAYESIAN INFERENCE Meaning and
Definition
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Bayesian inference refers to a statistical method used to make predictions or draw conclusions based on prior knowledge and observed data. It is named after Thomas Bayes, an 18th-century British mathematician, who introduced the concept of conditional probability.
In Bayesian inference, prior knowledge or beliefs are combined with observed data to update or revise our understanding of a particular situation. This process involves assigning a prior probability, which represents our initial beliefs or expectations about the event or parameter of interest, and combining it with the likelihood, which quantifies how likely the observed data would have been given different possible values of the event or parameter.
The combination of the prior probability and the likelihood is known as the posterior probability, which measures the updated belief or probability after considering the observed data. The posterior probability provides a measure of the confidence we have in our conclusions or predictions.
Bayesian inference is considered a more flexible approach compared to classical or frequentist statistics, as it allows the incorporation of prior knowledge into the analysis. This is particularly useful when there is limited data available or when including expert opinion can improve the accuracy of predictions. Additionally, Bayesian inference provides a framework for updating beliefs as more data becomes available, allowing for iterative learning and continuous refinement of predictions or conclusions.
Etymology of BAYESIAN INFERENCE
The etymology of the word "Bayesian" comes from the name of the mathematician Thomas Bayes, who lived in the 18th century. Bayes was known for his work on probability theory and is considered one of the pioneers of Bayesian statistics and inference.
The word "inference" comes from the Latin word "inferentia", which means "act of bringing in/near, drawing an inference". Inference refers to the process of drawing conclusions or making predictions based on evidence or reasoning.
When used together, "Bayesian inference" refers to the process of making statistical inferences or predictions using Bayesian probability theory. It involves updating prior beliefs or knowledge based on new evidence or data, using Bayes' theorem to calculate updated probabilities.
