Graphical models are a powerful framework for representing complex dependencies among random variables and building large-scale multivariate statistical models. They are widely used in machine learning and statistics to simplify the representation and computation of joint probability distributions through graph structures.

Graphical Models

Graphical models

complex dependencies

A random variable is a numerical outcome of a random phenomenon, serving as a bridge between probability theory and real-world scenarios by assigning numerical values to each outcome in a sample space. They are categorized into discrete and continuous types, each with specific probability distributions that describe the likelihood of their outcomes.

random variables

large-scale multivariate statistical models

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data to make informed decisions and predictions. It provides the tools to handle variability and uncertainty in data, enabling researchers and analysts to draw valid conclusions and insights from empirical evidence.

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