Howtosignalprocessing
- What is likelihood in Bayesian inference?
- What is the difference between maximum likelihood and Bayesian?
What is likelihood in Bayesian inference?
The likelihood of a hypothesis (H) given some data (D) is proportional to the probability of obtaining D given that H is true, multiplied by an arbitrary positive constant (K). In other words, L(H|D) = K · P(D|H).
What is the difference between maximum likelihood and Bayesian?
In other words, in the equation above, MLE treats the term p(θ)p(D) as a constant and does NOT allow us to inject our prior beliefs, p(θ), about the likely values for θ in the estimation calculations. Bayesian estimation, by contrast, fully calculates (or at times approximates) the posterior distribution p(θ|D).
