Big Data: Inference and sampling in distributed environments

Introduction

What this guide covers

Introduction Statistical Inference · Distributed Computing

Overview

Topics covered

• Introduction
• Inference and Sampling in Distributed Environments
• Fundamentals of Statistical Sampling in the Era of Big Data
• The myth of and Meng's paradox
• Overpopulation models and design-based inference
• Continuous Flow and Distributed Sampling Methods

Related concepts

Main connections

• Variance & standard deviation
• Bootstrap resampling
• Bayes' theorem & priors
• Mean & expected value
• Maximum likelihood estimation
• Regularization (Lasso / Ridge / Elastic Net)

Compatible notation

Mathematical sample in LaTeX

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