Lectures related to Consistent estimator

Estimators and Confidence Intervals

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

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Parameter Estimation: Detection & Estimation

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

Statistical Models and Parameter Estimation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Frequency Moments: Estimators and Algorithms

Covers the concept of frequency moments and introduces algorithms for estimating them efficiently.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Social and Information Networks: Processes

Covers computing measures over large networks, random walks, conductance bound, and epidemic models.

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Observers References

Explores the addition of a reference input to controllers and the design of autonomous and tracking-error estimators.

Confidence Intervals: Definition and Estimation

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

Path Integral Molecular Dynamics

Explores path integral molecular dynamics simulations, focusing on quantum kinetic energy estimators and convergence with the number of replicas.

Probabilistic Models for Linear Regression

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

Estimator of Variance

Explores variance estimation, creating personal estimators, correcting bias, and understanding Mean Square Error in statistical analysis.

Stochastic Simulations: Ergodicity and Estimators

Explores geometric ergodicity in Markov chains and estimators' bias and variance, highlighting efficiency loss quantification.

Parameter Estimation & Fisher Information

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.

Sampling Distributions: Estimation

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

Monte Carlo Estimation: Error Analysis

Covers the Monte Carlo method for generating realizations and unbiased estimators.

Network Sampling: Consistency, Models, and Dynamics

Explores network sampling consistency, models, and graph dynamics in real-life scenarios.

Supervised Learning Intro: MaxL Efficiency

Covers supervised learning efficiency, MaxL, unbiased estimators, MSE calculation, and large datasets.