Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Supervised Learning Intro: MaxL Efficiency
Graph Chatbot
Related lectures (53)
Intro to Quantum Sensing: Parameter Estimation and Fisher Information
Introduces Fisher Information for parameter estimation based on collected data.
Estimation and Confidence Intervals
Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.
Monte Carlo: Markov Chains
Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.
Sampling Distributions: Estimators and Variance
Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Confidence Intervals: Gaussian Estimation
Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.
Bias, Variance, Consistency, EMV
Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.
Statistical Estimation
Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Statistics for Data Science: Introduction to Statistical Methods
Covers the fundamental concepts of statistics and their application in data science.
Sampling Distributions: Theory and Applications
Explores sampling distributions, estimators' properties, and statistical measures for data science applications.
Point Estimation in Statistics
Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.
Estimators and Confidence Intervals
Log in to Mediaspace to watch this video
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Basic Principles of Point Estimation
Log in to Mediaspace to watch this video
Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.
Estimators and Bias
Log in to Mediaspace to watch this video
Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
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.
Optimality in Decision Theory: Unbiased Estimation
Log in to Mediaspace to watch this video
Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.
Statistical Models and Parameter Estimation
Log in to Mediaspace to watch this video
Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.
Estimators: Consistency and Efficiency
Log in to Mediaspace to watch this video
Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.
The Stein Phenomenon and Superefficiency
Log in to Mediaspace to watch this video
Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
Estimator of Variance
Log in to Mediaspace to watch this video
Explores variance estimation, creating personal estimators, correcting bias, and understanding Mean Square Error in statistical analysis.
Previous
Page 1 of 3
Next
