Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js

Probability

Description

notes on probability
Silvia Colombo
Note by Silvia Colombo, updated more than 1 year ago
Silvia Colombo
Created by Silvia Colombo over 10 years ago
22
0
1 2 3 4 5 (0)

Resource summary

Page 1

Probability Machine Learning and Pattern Recognition Chris Williams School of Informatics, University of Edinburgh August 2014 (All of the slides in this course have been adapted from previous versions by Charles Sutton, Amos Storkey, David Barber.) 1 / 31 Outline I What is probability? I Random Variables (discrete and continuous) I Expectation I Joint Distributions I Marginal Probability I Conditional Probability I Chain Rule I Bayes' Rule I Independence I Conditional Independence I Some Probability Distributions (for reference) I Reading: Murphy secs 2.1-2.4 2 / 31 What is probability? I Quantication of uncertainty I Frequentist interpretation: long run frequenies of events I Example: The probability of a particular coin landing heads up is 0.43 I Bayesian interpretation: quantify our degrees of belief about something I Example: the probability of it raining tomorrow is 0.3 I Not possible to repeat \tomorrow" many times I Basic rules of probability are the same, no matter which interpretation is adopted 3 / 31

Show full summary Hide full summary

0 comments

There are no comments, be the first and leave one below:

Similar

Probability S1
Alice Kimpton
Maths Exponents and Logarithms
Will Thorpe
New GCSE Maths required formulae
Sarah Egan
GCSE Maths: Statistics & Probability
Andrea Leyden
Counting and Probability
Culan O'Meara
Mathematics Prep for maths exam
Lulwah Elhariry
Probability
Dami Alvarez
Teoría de Conteo
ISABELLA OSPINA SAENZ
Probability
Ravindra Patidar
Probability & Statistics
Rohit Gurjar