Discrete sine transform

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Fourier analysis

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Related lectures (17)

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Signals & Systems I: Fourier Series Decomposition

Explores orthogonal sinusoids, Fourier coefficients, spectrum of signals, and complex Fourier series.

Sampling: maximum likelihood estimation

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Fourier Series: Periodic Functions

Explores Fourier series for periodic functions, emphasizing coefficient calculation and function representation using sine and cosine terms.

The Nested Logit Model

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

Mixture models: individual level parameters

Explores mixture models and individual-level parameters in discrete choice scenarios, covering distribution, Bayes theorem, and expected values.

Fourier Series: Periodic Functions and Sine/Cosine Interactions

Covers the Fourier series and interactions between sine and cosine functions.

Discrete choice and machine learning: two complementary methodologies

Explores discrete choice and machine learning as complementary methodologies, discussing supervised learning, model advantages, pitfalls, aggregation bias, probabilistic classification, and panel data.