Posterior probability

Related lectures (31)

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Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Explores the behavior of charged particles in magnetic fields and the invariance of quantum probabilities.

Covers conditional probability distributions and introduces the concept of conditional expected value.

Explores filter synthesis methods and the role of random variables in measurement systems.

Explores the Schrödinger equation for a particle in a potential well.

Covers Linear Discriminant Analysis (LDA) as a generative method for classification.

Explores time dependence in Quantum Mechanics, wavepackets, stationary states, and probability density.

Covers basic statistics concepts, confidence intervals, t distribution, and experimental study strategies.

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

Covers variational inference and neural networks for classification tasks.