Lectures related to Nuclear Power Safety Concerns

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Bayesian Estimation: Unsupervised Learning & MCMC

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

Maximum Likelihood Estimation: Properties and Consistency

Explores Maximum Likelihood Estimation properties, consistency, and applications in statistical inference.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Gaussian Processes: Designing Receivers

Covers the theory behind Gaussian processes and the design of receivers using MAP calculations.

Statistical Inference: Weighted Mean and Radioactive Decay

Introduces weighted mean calculation and radioactive decay concept with probability estimation.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Regular Exponential Family Models

Explores regular exponential family models, unifying distributions like Poisson, binomial, and normal under a common framework.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Maximum Likelihood Estimation: Theory and Applications

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Exponential Family

Covers the properties of the exponential family and the estimation of parameters.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Parametric Models: Estimation and Optimization

Explores parametric models, estimation techniques, regression models, and score-based classifiers in data analysis.

Statistical Estimation: Maximum Likelihood

Explores Maximum Likelihood Estimation properties, challenges, and alternative methods in statistical inference.

Interval Estimation: Method of Moments

Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.

Testing t-tests MOOC: Introduction to Discrete Choice Models

Introduces t-tests, focusing on hypothesis testing and comparing coefficients using the t-distribution.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Bias and Variance in Estimation

Discusses bias and variance in statistical estimation, exploring the trade-off between accuracy and variability.