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Related lectures (30)

Page 3 of 3

Statistical Estimators

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Hypothesis Testing: Q-Q Plots and Non-Parametric Tests

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Bayesian Inference: Gaussian Prior for Mean

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

The Nested Logit Model

Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.

Decision Rules: Maximum a Posteriori Decision

Explores decision rules based on likelihood ratios and the maximum a posteriori decision.

Bayesian Estimation

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Advanced Probabilities: Random Variables & Expected Values

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

Word Embedding Models: Optimization and Applications

Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.