Statistical Models and Parameter Estimation

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Related lectures (53)

Statistical Theory: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Statistical Theory: Fundamentals Log in to Mediaspace to watch this video

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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.

Spin Glasses and Bayesian Estimation Log in to Mediaspace to watch this video

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Law of Large Numbers: Strong Convergence Log in to Mediaspace to watch this video

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Statistical Estimators Log in to Mediaspace to watch this video

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Linear Regression Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

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

Covers the normal distribution, including its properties and calculations.

Point Estimation in Statistics Log in to Mediaspace to watch this video

Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.

L-Moment Estimation: Probability-Weighted Moments Log in to Mediaspace to watch this video

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE Log in to Mediaspace to watch this video

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Gaussian Mixture Models & Noisy Signals Log in to Mediaspace to watch this video

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Overfitting in Supervised Learning: Case Studies and Techniques Log in to Mediaspace to watch this video

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Estimation and Analysis of Deviance Log in to Mediaspace to watch this video

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Elements of Statistics: Probability and Random Variables Log in to Mediaspace to watch this video

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Conditional Probability: Understanding Events and Their Relationships Log in to Mediaspace to watch this video

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Applications of Quantum Science: Densities and Statistics Log in to Mediaspace to watch this video

Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.

Statistics for Civil Engineering Log in to Mediaspace to watch this video

Covers the fundamentals of statistics, exploring probabilistic models, variability, uncertainty, and data collection.

Logistic Regression: Probability Modeling Log in to Mediaspace to watch this video

Covers logistic regression for binary classification using probability modeling and optimization methods.

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