Editing Probabilistic Machine Learning (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Probability distribution''' — A function assigning probabilities to possible outcomes; the fundamental object of probabilistic ML.
* '''Prior''' — A distribution encoding beliefs before observing data: P(θ).
* '''Posterior''' — Updated beliefs after observing data: P(θ|D).
* '''Likelihood''' — The probability of the data given model parameters: P(D|θ).
* '''MAP (Maximum A Posteriori)''' — Finding the mode of the posterior; regularized point estimate.
* '''MLE (Maximum Likelihood Estimation)''' — Finding parameters maximizing P(D|θ); no prior.
* '''Probabilistic graphical model''' — Represents joint distributions over many variables using graph structure (Bayesian networks, Markov random fields).
* '''Bayesian network''' — A directed acyclic graph encoding conditional independence relationships.
* '''Hidden Markov Model (HMM)''' — A probabilistic sequence model with hidden states; classic for speech and bioinformatics.
* '''Variational Autoencoder (VAE)''' — A generative model using variational inference to learn a probabilistic latent space.
* '''Normalizing flow''' — A generative model constructed by composing invertible transformations to transform a simple distribution into a complex one.
* '''ELBO (Evidence Lower Bound)''' — The objective maximized in variational inference: log P(D) ≥ ELBO.
* '''Conformal prediction''' — A framework providing distribution-free prediction intervals with guaranteed coverage.
* '''Calibration''' — A probabilistic model is calibrated if its confidence scores match empirical frequencies.
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