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Discrete & Continuous | MIT Computational Thinking Spring 2021 | Lecture 14
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Contents
00:00 Introduction
00:30 Julia concepts
01:04 Pedagogical concepts
01:30 Discrete and Continuous
03:35 Heard in the hallways: I only like discrete math. I only like continuous math.
07:30 Indexing and Function Evaluation
12:06 Area
28:30 Area using inscribed squares
35:45 Discrete Random Walks and Brownian Motion
0:05:37
The Sum of Discrete and Continuous Random Variables
Discrete & Continuous | MIT Computational Thinking Spring 2021 | Lecture 14
0:11:09
L08.2 Probability Density Functions
0:18:37
Inferring a Discrete Random Variable from a Continuous Measurement
0:11:35
Inferring a Continuous Random Variable from a Discrete Measurement
0:05:13
L01.7 A Discrete Example
0:11:56
Discrete and continuous random variables | Probability and Statistics | Khan Academy
1:19:44
Lecture 18: Discrete Symmetries
0:12:48
L08.7 Cumulative Distribution Functions
0:09:18
L09.7 Joint PDFs
0:10:21
L05.3 Probability Mass Functions
0:07:52
L12.2 The Sum of Independent Discrete Random Variables
0:09:14
L05.2 Definition of Random Variables
0:04:35
L14.6 Discrete Parameter, Continuous Observation
0:05:08
L07.4 Independence of Random Variables
0:05:03
L01.3 Sample Space Examples
0:16:17
Probability Distribution Functions (PMF, PDF, CDF)
0:03:36
Types of Variables: Discrete, Continuous & Categorical
1:17:41
5. Stochastic Processes I
0:09:56
L09.2 Conditioning A Continuous Random Variable on an Event
1:01:35
Sample Class: Class 12--Bayesian Updating: Discrete Priors
0:02:09
L24.2 Introduction to Markov Processes
0:04:06
L05.5 Uniform Random Variables
0:06:05
Uniform Distribution EXPLAINED with Examples
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