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Concept

Continuous or discrete variable

Related lectures (31)

Covers the time evolution for non-time invariant systems and continuous and discrete degrees of freedom.

Introduces Variational Monte Carlo methods for discrete and continuous systems, including Jastrow states and Markov-chain Monte Carlo algorithms.

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Explores binomial distribution, random walk, and diffusion concepts in a continuous context.

Covers linear regression, from developing research questions to interpreting R-squared and adding predictors to improve the model.

Covers geometric properties of discrete variables in GIS, including location, size, and shape.

Covers difference equations, dynamic systems, linearity, and impulse response in discrete systems.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Covers the geometric properties of discrete variables in geographic information systems, focusing on shape, location, and size indices.

Explores the shift from continuous to discrete control systems, focusing on the challenges and benefits of digital implementation.

Covers discrete variables in geographic information systems and their geometric properties, including spatial arrangement and autocorrelation.

Covers the basics of optimization modeling using AMPLIDE.

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Explores partial derivatives in composing functions, continuity proofs, mean value theorem applications, and integral calculus implications.

Explores the set covering problem through the example of completing a sticker collection at a minimum cost.

Introduces the backpack problem, a discrete optimization problem with constraints and algorithms to solve it.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Covers geometric properties, spatial arrangement, autocorrelation, and neighbourhood analysis in geographic information systems.