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Statistical Models: Basics & Applications

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Related lectures (43)

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Stochastic Models for Communications Log in to Mediaspace to watch this video

Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.

Probability Distributions in Environmental Studies Log in to Mediaspace to watch this video

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Probability Distributions: Basics and Properties Log in to Mediaspace to watch this video

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Confidence Intervals and Hypothesis Testing Log in to Mediaspace to watch this video

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

Normal Distribution: Properties and Calculations Log in to Mediaspace to watch this video

Covers the normal distribution, including its properties and calculations.

Probability and Statistics: Basics and Applications Log in to Mediaspace to watch this video

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Expectation and Variance Log in to Mediaspace to watch this video

Explores expectation and variance for discrete random variables, emphasizing properties and practical applications.

Gaussian Mixture Models: Data Classification Log in to Mediaspace to watch this video

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Poisson Paradigm: Dependency Measures Log in to Mediaspace to watch this video

Explores the Poisson Paradigm and dependency measures in ordered pairs and graphs.

Probability Distributions: Independence and Linear Combinations Log in to Mediaspace to watch this video

Explores independence, linear combinations of variables, and probability laws.

Joint Probability and Marginal Laws Log in to Mediaspace to watch this video

Explores joint probability, marginal laws, conditional laws, and potential paradoxes in probability theory.

Mock Exam Session: Instructions and Solutions Log in to Mediaspace to watch this video

Guides students through a mock exam session and provides insights into solving probability and discrete mathematics exercises.

Probability: Independence Log in to Mediaspace to watch this video

Explores the concept of independence in probability theory, showing how events can occur without influencing each other.

Data Standardization and Error Handling in Julia Log in to Mediaspace to watch this video

Covers tips and tricks for working with Julia, focusing on setting up the environment, handling errors, and standardizing dataframes.

Conditional Probability Log in to Mediaspace to watch this video

Covers the concept of conditional probability and its practical applications.

Joint Gaussian Variables: Properties and Convergence Log in to Mediaspace to watch this video

Covers joint Gaussian variables' properties and convergence concepts.

Introduction to Structural Mechanics: Matching Conditions Log in to Mediaspace to watch this video

Explores matching conditions in structural mechanics for non-straight beams, frames, and arches, emphasizing differential relations and constant determination.

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