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Lecture
Special Families of Models
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Related lectures (30)
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Generalized Linear Models
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
Sufficient Statistics: Understanding Data Compression
Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.
Continuous Random Variables
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Sampling Distributions: Theory and Applications
Explores sampling distributions, estimators' properties, and statistical measures for data science applications.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Probability and Statistics
Covers probability distributions, moments, and continuous random variables.
Air Pollution Analysis
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
Exponential Family
Covers the properties of the exponential family and the estimation of parameters.
Advanced Probabilities: Random Variables & Expected Values
Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.
Introduction to Continuous Random Variables: Probability Distributions
Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.
