Probability mass function

Chapter 3 Probability mass functions

Think Stats

The code for this chapter is in probability.py . For information about downloading and working with this code, see Section 0.2 . 3.1 Pmfs Another way to represent a distribution is a probability mass ...

Probability Mass and Density Functions

Towards Data Science

Probability mass and density functions are used to describe discrete and continuous probability distributions, respectively. This allows us to determine the probability of an observation being…

Distributions

Think Bayes

In the previous chapter we used Bayes’s Theorem to solve a cookie problem; then we solved it again using a Bayes table. In this chapter, at the risk of testing your patience, we will solve it one mor...

Part 03: Describing Random Outcomes: PMF, CDF, and PDF

Towards AI

In the previous article, we introduced the concept of a random experiment using the example of student marks in a class. Now, we will delve deeper into how we mathematically describe the likelihood of...

Chapter 6 Probability density functions

Think Stats

The code for this chapter is in density.py . For information about downloading and working with this code, see Section 0.2 . 6.1 PDFs The derivative of a CDF is called a probability density function ,...

What Is A Probability Density Function?

Towards Data Science

In the wonderful world of statistics, distributions are an absolutely vital component that sits at the center of a universe of mathematics. Distributions are used to describe data mathematically, and…...

The Building Blocks of Probability Made Simple

Towards Data Science

Essentially every event A gets assigned a real number ℙ(A). In other words, it is a mapping that maps some event to some real number. ℙ is often referred to as a probability measure. ℙ is considered…

Probability & Statistics for Beginners in Machine Learning: Part 3 — Probability Distribution

Analytics Vidhya

A probability distribution is the mathematical function through which the probability of occurrence of different possible outcomes in an experiment can be calculated. Some very common examples we can…...

Probabilistic Matrix Factorization

Towards Data Science

In this post we introduce probability matrix factorization from a Bayesian Statistics perspective. We also draw connections between optimization and regularization in posterior inference.

Probability Theory for Deep Learning

Towards Data Science

A very quick introduction to Random variables, probability mass/density functions, and special distribution functions.

Statistical Distributions

Towards Data Science

A probability distribution is a mathematical function that provides the probabilities of the occurrence of various possible outcomes in an experiment. Probability distributions are used to define…

Union of Probabilistic Event Groups

Towards Data Science

Probability is the measure of the likelihood that an event will occur in a Random Experiment. Event is the representation of a subset of the sample space (set of all possible results of the…