Explore the fundamentals of probability distributions through Python code in this 19-minute PyCon US talk by Eric J. Ma. Gain a solid understanding of probability distributions as objects in modeling contexts, learn about sampling from distributions, and demystify concepts like joint, conditional, and marginal distributions. Discover how probability distributions function as number generators and data models, understand the relationship between probability and area under curves, and grasp the distinction between probability density functions (PDF) and probability mass functions (PMF). By the end of this talk, acquire a working knowledge of probability to delve deeper into Bayesian statistics.
A Careful Walk Through Probability Distributions, Using Python
Overview
Syllabus
Intro
Novartis Institutes for Biomedical Research
Insight Health Data Fellow
MIT Biological Engineering
Probability distributions are number generators!
generative models of data
What's the probability of a single value?
Probability Area under PDF
A probability distribution gives rules for drawing random numbers
Probability distributions have a PDF/PMF
Probability Area under curve
Likelihood Height of curve
Taught by
PyCon US
