Bayesian Estimation: Unsupervised Learning & MCMC

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Related lectures (32)

Page 3 of 4

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Maximum Likelihood, MSE, Fisher Information, Cramér-Rao Bound

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Statistical Inference: Weighted Mean and Radioactive Decay

Introduces weighted mean calculation and radioactive decay concept with probability estimation.

Estimators and Bias

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Estimation and Confidence Intervals

Explores parameter estimation, standard errors, and confidence intervals using the central limit theorem and practical examples.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Statistical Thermodynamics: Boltzmann Distribution

Covers the Boltzmann distribution in statistical thermodynamics, focusing on the harmonic oscillator system and analyzing state occupancy with varying temperatures.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.