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Detection & Estimation
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Related lectures (39)
Probability and Statistics: Basics and Applications
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Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
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Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Jacamar Data Analysis
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Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
Introduction to Inference
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Covers the basics of probability theory, random variables, joint probability, and inference.
Probability and Statistics
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Explores joint random variables, conditional density, and independence in probability and statistics.
Probability and Statistics: Fundamentals
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Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Conditional Density and Expectation
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Explores conditional density, expectations, and independence of random variables with practical examples.
Likelihood Ratio Tests: Optimality and Applications
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Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.
Normal Distribution: Properties and Calculations
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Covers the normal distribution, including its properties and calculations.
Spin Glasses and Bayesian Estimation
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Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.
Estimators and Confidence Intervals
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Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Estimating Extreme Quantiles
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Covers the estimation of extreme quantiles using empirical quantiles and sample data.
Residue Theorem: Applications in Complex Analysis
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Discusses the residue theorem and its applications in calculating complex integrals.
Logistic Regression: Cost Functions & Optimization
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Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.
Probability Theory: Independence and Conditional Independence
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Explores independence and conditional independence in probability theory through mathematical definitions and examples.
Logistic Regression: Probabilistic Interpretation
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Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.
Estimating R: Moments and Cumulants
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Explores the estimation of R using moments and cumulants in extreme value analysis.
Modern Regression: Maximum Likelihood Estimation
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Explores maximum likelihood estimation, profile log likelihood, inference on coefficients, quasi-likelihood, model comparison, and REML method.
Probability Distributions: Discrete Random Variables
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Discusses properties and functions of discrete random variables, including binomial and negative binomial distributions.
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