Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Discusses risk measure evaluation, confidence intervals, and multivariate distributions for portfolio risk assessment.
Explores continuous random variables and their properties, including support and cumulative distribution functions.
Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Delves into advanced probability theory, covering inequalities, trials, distributions, and calculations for probabilities and expectations.
Covers properties and transformations of discrete random variables, focusing on PMF and expectation.
Covers the transformations of joint continuous densities and their implications on probability distributions.
