Conditional probability Archives

When Dependence Between Events Is Conditional

Posted on November 26, 2016 Written by The Cthaeh 9 Comments

In this post, I want to talk about conditional dependence and independence between events. This is an important concept in probability theory and a central concept for graphical models.

In my two-part post on Bayesian belief networks, I introduced an important type of graphical models. You can read Part 1 and Part 2 by following these links.

This is actually an informal continuation of the two Bayesian networks posts. Even though I initially wanted to include it at the end of Part 2, I decided it’s an important enough topic that deserves its own space.

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What Are Bayesian Belief Networks? (Part 2)

Posted on November 20, 2016 Written by The Cthaeh 11 Comments

In the first part of this post, I gave the basic intuition behind Bayesian belief networks (or just Bayesian networks) — what they are, what they’re used for, and how information is exchanged between their nodes.

In this post, I’m going to show the math underlying everything I talked about in the previous one. It’s going to be a bit more technical, but I’m going to try to give the intuition behind the relevant equations.

If you stick to the end, I promise you’ll get a much deeper understanding of Bayesian networks. To the point of actually being able to use them for real-world calculations.

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What Are Bayesian Belief Networks? (Part 1)

Posted on November 3, 2016 Written by The Cthaeh 22 Comments

In my introductory Bayes’ theorem post, I used a “rainy day” example to show how information about one event can change the probability of another. In particular, how seeing rainy weather patterns (like dark clouds) increases the probability that it will rain later the same day.

Bayesian belief networks, or just Bayesian networks, are a natural generalization of these kinds of inferences to multiple events or random processes that depend on each other.

This is going to be the first of 2 posts specifically dedicated to this topic. Here I’m going to give the general intuition for what Bayesian networks are and how they are used as causal models of the real world. I’m also going to give the general intuition of how information propagates within a Bayesian network.

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Calculating Compound Event Probabilities

Posted on April 29, 2016 Written by The Cthaeh 4 Comments

You can think of probabilities as measures of uncertainty in the occurrence of an event, the truth of a hypothesis, and so on.

These measures are numbers between 0 and 1. Zero means the event is impossible to occur and 1 means the event is certain to occur.

If you have many events of interest, you can measure their probabilities separately, but you can also measure probabilities of different combinations of these events.

Say you’re following the national soccer championships of England, Spain, and Italy. You want to calculate the probabilities of Arsenal, Barcelona, and Juventus becoming national champions next season. These probabilities are:

P(Arsenal)
P(Barcelona)
P(Juventus)

But what if you want to calculate the probability of both Arsenal and Barcelona becoming champions? Or the probability that at least one of the three teams does?

In this post, I’m going to show how probabilities of such combinations of events are calculated. I’m going to give the general formulas, as well as the intuition behind them. To do that, I’m first going to introduce a few relevant concepts from probability theory.

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Bayes’ Theorem: An Informal Derivation

Posted on February 28, 2016 Written by The Cthaeh 12 Comments

If you’re reading this post, I’ll assume you are familiar with Bayes’ theorem. If not, take a look at my introductory post on the topic.

Here I’m going to explore the intuitive origins of the theorem. I’m sure that after reading this post you’ll have a good feeling for where the theorem comes from. I’m also sure you will find the simplicity of its mathematical derivation impressive. For that, some familiarity with sample spaces (which I discussed in this post) would come in handy.

So, what does Bayes’ theorem state again?

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What Is a Sample Space?

Posted on February 25, 2016 Written by The Cthaeh Leave a Comment

The concept of a sample space is fundamental to probability theory. It is the set of all possibilities (or possible outcomes) of some uncertain process.

For example, the sample space of the process of flipping a coin is a set with 2 elements. Each represents one of the two possible outcomes: “heads” and “tails”. The sample space of rolling a die is a set with 6 elements and each represents one of the six sides of the die. And so on. Other terms you may come across are event space and possibility space.

Before getting to the details of sample spaces, I first want to properly define the concept of probabilities. [Read more…]