Lectures related to Elements of Statistics: Memorylessness and Stationary Processes

Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE

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

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Time Series: Estimation and Spectral Representation

Explores time series estimation, spectral representation, and p-variate analysis in depth.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Structure of Measure-Preserving Systems

Covers the abstract structure of measure-preserving systems and aims to understand their classification and ergodic decompositions.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Maximum Likelihood Estimation

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

Maximum Likelihood Estimation: Theory and Examples

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Maximum Likelihood Estimation: Properties and Applications

Covers Maximum Likelihood Estimation properties, applications, and assumptions, providing a comprehensive understanding of MLE concepts and their practical implications.

Statistical Theory: Maximum Likelihood Estimation

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

Maximum Likelihood Estimation: Properties and Consistency

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

Maximum Likelihood Estimation

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Continuous-Time Stochastic Processes: Ergodicity Examples

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Gaussian Process Regression: Probabilistic Linear Regression

Explores Probabilistic Linear Regression and Gaussian Process Regression, emphasizing kernel selection and hyperparameter tuning for accurate predictions.

Efficiency of Sampling: Ergodicity and Autocorrelation Functions

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Ergodic properties of low complexity symbolic systems

Explores the influence of complexity on ergodic properties of symbolic systems, presenting the Curtis-Hedlund-Lyndon Theorem and constructions of minimal subshifts.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Correlations of the Liouville function

Explores correlations of the Liouville function along deterministic and independent sequences, covering key concepts and theorems.