10 docs tagged with "probability"

Bernoulli, Binomial, Multinomial, Gaussian, Exponential, Beta, Dirichlet - the probability distributions that appear throughout machine learning and which model outputs them.

Markov, Chebyshev, and Hoeffding inequalities, the Central Limit Theorem, and the Law of Large Numbers - bounding probabilities and understanding generalization in machine learning.

Conditional probability, Bayes' theorem, prior and posterior, total probability - the engine behind Naive Bayes, Bayesian inference, and generative vs discriminative model design.

Expected value, linearity of expectation, variance, covariance, and higher moments - the summary statistics that define how ML models behave over data distributions.

Joint distributions, marginalization, conditional distributions from joint, independence, covariance matrices, and their role in graphical models and latent variable models.

How probability theory underpins every machine learning algorithm - from loss functions to generative models to uncertainty quantification.

Framing machine learning through probability - MLE, MAP estimation, prior-posterior reasoning, cross-entropy as negative log-likelihood, calibration, Bayesian deep learning, and uncertainty quantification.

Kolmogorov axioms, sample spaces, events, conditional probability, and independence - the formal foundations of all probabilistic reasoning in machine learning.

Discrete and continuous random variables, PMFs, PDFs, CDFs, and transformations - the formal tools for describing model outputs as probability distributions.

Inverse CDF, rejection sampling, importance sampling, MCMC, and Monte Carlo integration - the algorithms that power Bayesian inference, data augmentation, and generative modeling.