Learning Bayesian Networks
author:
Richard E. Neapolitan,
Northeastern Illinois University
Description
Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. The 1990's saw the emergence of excellent algorithms for learning Bayesian networks from passive data. I will discuss the constraint-based learning method using an intuitive approach that concentrates on causal learning. Then I will discuss the Bayesian approach with some simple examples. I will show how, using the Bayesian approach, we can even learning something about causal influences from passive data on two variables. Finally, I will show some applications to finance and marketing.
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| Slides | |
| 0:03 | Statistical Causality |
| 1:23 | slide 2 |
| 2:46 | A common way to learn (perhaps define) causation is via manipulation experiments (non-passive data) |
| 4:30 | slide 4 |
| 5:03 | slide 5 |
| 5:33 | slide 6 |
| 6:20 | slide 7 |
| 7:22 | slide 8 |
| 8:05 | Causal Graphs |
| 10:36 | The Causal Markov Assumption |
| 12:41 | Examples |
| 18:35 | slide 12 |
| 20:14 | slide 13 |
| 24:18 | slide 14 |
| 29:24 | Experimental evidence for the Causal Markov Assumption |
| 29:42 | Exceptions to the Causal Markov Assumption |
| 29:45 | 1. Hidden common causes |
| 31:21 | 2. Causal feedback |
| 31:44 | 3. Selection bias |
| 34:22 | 4. The entities in the population are units of time |
| 35:24 | slide 21 |
| 35:41 | slide 22 |
| 39:31 | Causal Faithfulness Assumption |
| 40:43 | Exceptions to the Causal Faithfulness Assumption |
| 41:13 | Learning Causal Influences Under the Causal Faithfulness Assumption |
| 41:33 | slide 26 |
| 42:32 | Example |
| 43:18 | slide 28 |
| 43:43 | Example 1 |
| 45:18 | Example 2 |
| 46:18 | Example 3 |
| 51:48 | Example 4 |
| 53:29 | Theorem |
| 54:14 | Example 5 |
| 57:39 | How much data do we need? |
| 59:17 | Empirical Results |
| 60:35 | Conflicting Empirical Results |
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This is a very good introduction to causality and learning bayesian networks. there are many examples which are explained in detail. the lecture is also a very good introduction and supplement to neapolitans book "learning bayesian networks" which can be found here: http://www.amazon.com/Learning-Bayesi... the book is very good but also very expensive. i bought it and kind of regretted it later because of the price. combined with this lecture it might be worth it :)