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1
Intro
2
Uncertainty
3
Maximizing Rewards
4
Why Probability
5
Means and Variances
6
Improbability
7
Bayesian Reasoning
8
Experiments
9
Probability distributions
10
gaussians
11
multivariate Gaussians
12
Poisson distributions
13
Bayesian update
14
Graphical models
15
Formants
16
Graphical Model
17
Hidden Markov Model
18
Markov Chain
19
Summary
Description:
Explore the fundamental concepts of probability in this 43-minute tutorial from the MIT BMM Summer Course 2018, presented by Andrei Barbu. Dive into topics such as uncertainty, maximizing rewards, means and variances, Bayesian reasoning, and probability distributions including Gaussians and Poisson. Examine graphical models, hidden Markov models, and Markov chains, while learning about their applications in areas like formant analysis. Gain a comprehensive understanding of probability theory and its practical implications through experiments and real-world examples.

Probability Tutorial

MITCBMM
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