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Related lectures (32)

Elementary Distributions Factsheet

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

Linear Regression: Basics and Applications

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Regular Exponential Family Models

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.

Orthogonal Families and Projections

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Least Squares Solutions

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

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Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Matrix Operations: Product and Inverse

Covers matrix operations, focusing on the product and inverse of matrices.

Singular Value Decomposition

Covers the Singular Value Decomposition theorem and its applications in practice.

Variable Selection Methods

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

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Vector Spaces: Definitions and Examples

Covers the definition and examples of vector spaces, including subspaces and linear transformations.

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Sampling Theory: Statistics and Inference

Covers sampling theory, statistics, and inference, focusing on the sampling distribution of statistics.

Sufficient Statistics: Understanding Data Compression

Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.

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Singular Value Decomposition

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

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