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📚TheoryIntermediate

Maximum Likelihood & Generative Models

Maximum Likelihood Estimation (MLE) picks parameters that make the observed data most probable under a chosen probabilistic model.

#maximum likelihood#generative models#naive bayes+12
∑MathIntermediate

Sufficient Statistics

A sufficient statistic compresses all information in the sample about a parameter into a lower-dimensional summary without losing inferential power.

#sufficient statistic
Advanced
Filtering by:
#likelihood
#fisher neyman factorization
#exponential family
+12
📚TheoryIntermediate

Bayesian Inference

Bayesian inference updates prior beliefs with observed data to produce a posterior distribution P(\theta\mid D).

#bayesian inference#posterior#prior+12
∑MathIntermediate

Maximum A Posteriori (MAP) Estimation

Maximum A Posteriori (MAP) estimation chooses the parameter value with the highest posterior probability after seeing data.

#map estimation#posterior mode#bayesian inference+12
∑MathIntermediate

Bayes' Theorem

Bayes' Theorem tells you how to update the probability of a hypothesis after seeing new evidence.

#bayes' theorem#posterior probability#prior probability+11