**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Lecture# Probability and Statistics

Description

This lecture covers the fundamental concepts of probability and statistics, providing mathematical tools for the study of random events, network modeling, stochastic algorithms, and more. Topics include distributions, expectation, variance, statistical inference, likelihood, and combinatorics. The course also discusses the importance of understanding material rather than memorization during exams.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

In course

Instructors (2)

MATH-232: Probability and statistics

A basic course in probability and statistics

Related concepts (241)

Summary statistics

In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in a measure of location, or central tendency, such as the arithmetic mean a measure of statistical dispersion like the standard mean absolute deviation a measure of the shape of the distribution like skewness or kurtosis if more than one variable is measured, a measure of statistical dependence such as a correlation coefficient A common collection of order statistics used as summary statistics are the five-number summary, sometimes extended to a seven-number summary, and the associated box plot.

Mathematical statistics

Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory. Statistical data collection is concerned with the planning of studies, especially with the design of randomized experiments and with the planning of surveys using random sampling.

Probability density function

In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would be equal to that sample.

Conditional probability distribution

In probability theory and statistics, given two jointly distributed random variables and , the conditional probability distribution of given is the probability distribution of when is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value of as a parameter. When both and are categorical variables, a conditional probability table is typically used to represent the conditional probability.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Related lectures (1,000)

Probability and StatisticsMATH-232: Probability and statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Probability and Statistics for SICMATH-232: Probability and statistics

Delves into probability, statistics, random experiments, and statistical inference, with practical examples and insights.

Probability and StatisticsMATH-232: Probability and statistics

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

Probability and Statistics: Independence and Conditional ProbabilityMATH-232: Probability and statistics

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Probability and StatisticsMATH-232: Probability and statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.