Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called *Bayesian* reasoning. Maybe you're a girl looking for a boyfriend, but the boy you're interested in refuses to date anyone who "isn't *Bayesian*".

They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... What matters is that Bayes is cool, and if you don't know Bayes, you aren't cool.

The **formula** will be stated after we examine the calculation from Example 1. Example 2 is presented at the end of the post and is left as exercise.

For a basic discussion of the Bayes’ **formula**, see [1] and chapter 4 of [2].

**Bayesian** reasoning is apparently one of those things which, like quantum mechanics or the Wason Selection Test, is inherently difficult for humans to grasp with our built-in mental faculties. Here you will find an attempt to offer an explanation of **Bayesian** reasoning - an excruciatingly gentle introduction that invokes all the human ways of grasping numbers, from natural frequencies to spatial visualization.

The intent is to convey, not abstract rules for manipulating numbers, but what the numbers mean, and why the rules are what they are (and cannot possibly be anything else).

Let's say you assume height is Normally distributed with a mean and variance and you want to estimate the posterior distribution of the mean and variance given the height.

So: $P(data|model) = L(\lambda) = \exp$ And $P(model) = g(\lambda = 1) = e^$ Therefore $P(model|data) = \exp\ e^$ And if I had a sample of $k_1 = j$, then $P(\lambda|k_1 = j) = \exp(-\lambda \log \lambda j - \log j! How do I calculate an expected value for the parameter?

After some research on the Internet I have found two *formulas* to get a good ranking system.

We open up a discussion of the Bayes’ **formula** by going through a basic example.

The Bayes’ **formula** or theorem is a method that can be used to compute “backward” conditional probabilities such as the examples described here.

I have tried to explain the problem using a simple reviews table and SQL queries, here's the reviews table.

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