Exponential Family

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Related lectures (32)

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Exponential Family: Maximum Entropy Distribution

Covers exponential families, maximum entropy, and Moxwell-Boltzmann distribution properties.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Maximum Likelihood Estimation: Properties and Applications

Covers Maximum Likelihood Estimation properties, applications, and assumptions, providing a comprehensive understanding of MLE concepts and their practical implications.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Information Measures

Covers variational representation and information measures such as entropy and mutual information.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Kinetic Theory: Distribution Function Evolution

Explores the evolution of the distribution function describing particles in phase space.

Binary Choice Model

Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Regulation of Therapeutic Products

Explores the regulation of therapeutic products, covering legal requirements, product classification, market access, and advertising rules.

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