YoVDO

When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Part 2

Offered By: Institut Henri Poincaré via YouTube

Tags

Statistical Inference Courses Data Analysis Courses Machine Learning Courses Probability Theory Courses Word Embeddings Courses Low-Rank Approximation Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore advanced techniques for making accurate inferences about complex distributions when sample sizes are insufficient for empirical distributions to be reliable. Delve into three key problems: optimally de-noising empirical distributions to improve accuracy, estimating population spectra from limited high-dimensional data, and recovering low-rank approximations of probability matrices from observed count data. Learn about an instance-optimal learning algorithm for distribution approximation, methods for estimating unseen elements in larger samples, and applications to genomics. Examine approaches for accurately estimating covariance matrix eigenvalues in high-dimensional settings with limited samples. Investigate techniques for matrix recovery problems related to community detection and word embeddings. Gain insights from cutting-edge research on overcoming data limitations in statistical inference and machine learning applications.

Syllabus

When your big data seems too small: accurate inferences beyond the empirical distribution 2/2


Taught by

Institut Henri Poincaré

Related Courses

Introduction to Statistics: Probability
University of California, Berkeley via edX
Aléatoire : une introduction aux probabilités - Partie 1
École Polytechnique via Coursera
Einführung in die Wahrscheinlichkeitstheorie
Johannes Gutenberg University Mainz via iversity
Combinatorics and Probability
Moscow Institute of Physics and Technology via Coursera
Probability
University of Pennsylvania via Coursera