Final answer:
Bayesian inference consists of two components: prior probability and likelihood. It updates prior knowledge with new evidence to compute a posterior probability, reflecting the updated beliefs about a parameter or hypothesis.
Step-by-step explanation:
Bayesian inference describes a learning process that consists of two primary components: prior probability and likelihood. The prior probability, represented as P(0), reflects what is known about the parameter or hypothesis before considering current data. Likelihood is the probability of observing the current data given a particular parameter or hypothesis, expressed as P(x|0). By applying Bayes' theorem, we update the prior knowledge with the new evidence (likelihood) to obtain a posterior probability, P(0|x), which reflects the updated belief about the parameter or hypothesis after considering the data.
Bayesian inference is grounded in the principle that our beliefs should be updated as new data becomes available, and it blends prior knowledge and current data to make informed decisions or predictions. This contrasts with frequentist inference, which relies solely on the data at hand and does not take prior belief into account. Bayesian approaches to learning and decision-making are more flexible and often offer more robust insights, especially in complex or uncertain environments.