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
Maximum Likelihood Estimator: Expression and Gradient
Graph Chatbot
Related lectures (50)
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.
Robust Optimization: Polynomial Approximation & Uncertainty Sets
Log in to Mediaspace to watch this video
Explores robust optimization through polynomial approximation and uncertainty sets, including robust linear programs and optimization tricks.
The Simplex Algorithm: Efficiency and Degeneracy
Log in to Mediaspace to watch this video
Covers the Simplex Algorithm, focusing on efficiency and degeneracy in linear optimization problems.
Convex Optimization Tutorial: KKT Conditions
Log in to Mediaspace to watch this video
Explores KKT conditions in convex optimization, covering dual problems, logarithmic constraints, least squares, matrix functions, and suboptimality of covering ellipsoids.
Linear Programming Duality
Log in to Mediaspace to watch this video
Explores the concept of duality in linear programming and its practical implications in optimization.
Fit Gumbel Curves to Rainfall Data
Log in to Mediaspace to watch this video
Explores fitting Gumbel curves to rainfall data in hydrology.
Convex Sets: Mathematical Optimization
Log in to Mediaspace to watch this video
Introduces convex optimization, covering convex sets, solution concepts, and efficient numerical methods in mathematical optimization.
Gradient Descent: Optimization and Constraints
Log in to Mediaspace to watch this video
Discusses gradient descent for optimization with equality constraints and iterative convergence criteria.
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.
Maximum Likelihood Estimation: Theory
Log in to Mediaspace to watch this video
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
Previous
Page 3 of 3
Next