Overview
Bayesian inference is a powerful statistical method that allows for the updating of probabilities as new evidence is introduced. It is grounded in Bayes' Theorem, which mathematically describes how to combine prior knowledge with new data to refine predictions. This approach is widely applicable acr...
Key Terms
Example: If you believe there is a 70% chance of rain based on past data, that's your prior probability.
Example: After seeing dark clouds, you might update the probability of rain to 90%.
Example: The likelihood of seeing dark clouds if it is going to rain.
Example: P(H|E) = (P(E|H) * P(H)) / P(E)
Example: Used in Bayesian statistics to estimate posterior distributions.
Example: Weather forecasts serve as evidence for predicting rain.
Related Topics
Frequentist Statistics
An approach to statistics that relies on the frequency of events to make inferences.
intermediateMachine Learning
A field of study that uses algorithms to learn from data and make predictions.
advancedStatistical Inference
The process of drawing conclusions about a population based on sample data.
intermediateKey Concepts
Prior ProbabilityLikelihoodPosterior ProbabilityBayes' Theorem