Towards the end of the post Bayes’ Rule, I eluded a bit to how Bayes’ rule becomes extremely powerful in Bayesian inference. The link happens when we start to interpret the variables of Bayes’ rule as parameters ($\theta$) of a model and observed data ($D$):

\begin{align} P(X\ |\ Y) &= \frac{P(Y\ |\ X)\ P(X)}{P(Y)} \\ P(\theta\ |\ D) &= \frac{P(D\ |\ \theta)\ P(\theta)}{P(D)} \end{align}

In this post, we will learn exactly how Bayes’ rule is used in Bayesian inference by going through a specific example of coin tossing. A lot of this post and examples are inspired by John K. Kruschke’s “Doing Bayesian Data Analysis”. An incredible book that I have been using for my entry into world of Bayesian statistics. I would highly recommend anyone interested in Bayesian statistics to get this book.

Important!

Please ensure that you are familiar with Bayes' rule before continuing as this post will not make sense with a thorough understanding of it. If you are not familiar, please review my previous post on Bayes' Rule for details.

Here is an overview of what will be discussed in this post.

The Coin Flipping Example

Consider the scenario where you found a coin on the side of a street that had an odd looking geometry, unlike anything you have ever seen before. It still has two sides (heads and a tail), and you start to wonder:

What is probability of getting a head on a given flip with this coin?

Given your knowledge of how a typical coin is, your prior guess is that is should be probably 0.5. But given the strange looking geometry, you also entertain the idea that it could be something like 0.4 or 0.6, but think these values are less probable than 0.5. You then proceed to flip the coin 100 times (because you are really bored and have time on your hands) and you notice that you get 83 heads. Given this observed data now, what is your guess at the probability of getting a head on a given flip?

If we think back to our examples from the Bayes’ Rule post, we can see that this particular example is not that dissimilar:

• We have a prior belief of what the probability of getting a heads is (0.5).
• We have some observed data, which is 83 heads out of 100 coin tosses.
• We need to update our belief of this probability now that we have some observed data.

There is however one key difference in this particular example compared to our previous examples. Our prior is not a single fixed value, but rather a series of different possible values. It could be 0.5 or 0.4 or 0.6….in fact it could be any value between 0 and 1! Moreover, the possible values are not all equally likely. For instance, we have a strong belief that it could be 0.5 (because of what we know about coins in general), and while 0.4 and 0.6 or any other value is possible we still think 0.5 is more probable. Visually, we could have something like:

Here we have 10 different possibilities of theta and their associated probabilities. In fact, what we have just described is a probability distribution that was defined in the Bayes’ Rule! So if we think of our prior as a random variable, $\theta$, then what we actually have is a prior probability distribution. What implications does this have? Well this ends up affecting how we measure the likelihood because before our likelihood was based on a single prior value. Instead, our likelihood essentially becomes a function of a series of candidate $\theta$ values. In other words, the probability of seeing the observed data is different depending on what the value of $\theta$ is.

Steps of Bayesian Inference

Now that we have given this simple example of a situation, we can walk through an example of how we can answer the aforementioned question (i.e. What is probability of getting a head on a given flip with this coin?) in a Bayesian way. To do any Bayesian inference, we follow a 4 step process:

1. Identify the observed data you are working with.
2. Construct a probabilistic model to represent the data (likelihood).
3. Specify prior distributions over the parameters of your probabilistic model (prior).
4. Collect data and apply Bayes’ rule to re-allocate credibility across the possible parameter values (posterior).

Let us walk through these steps one by one in the context of this example.

Step 1: Identify the Observed Data

In this example, we have a coin that when flipped gives us one of two possible outcomes: heads or tails. We can use the random variable, Y, to denote the outcome and assign it to 1 if it is heads and 0 if it is tails. These values are not ordinal, and merely represent a simplified way to represent heads or tails.

Step 2: Construct a Probabilistic Model to Represent the Data

Once we have identified the observed data, our next step is to come up with a probabilistic model along with meaningful parameters that can properly describe this data. The idea here is that the model is supposed to represent some process that generated this data (i.e. generative process). For this particular example, we can represent the probability of getting a head as $\theta$. Formally we can describe this as:

$$P(Y = 1\ |\ \theta) = \theta$$

It then follows that the probability of getting a tail is:

$$P(Y = 0\ |\ \theta) = 1 - \theta$$

These two expressions can be combined into a single expression:

$$P(Y = y\ |\ \theta) = \theta^{y}(1 - \theta)^{1 - y}$$

Notice how when $Y = 1$, this expression becomes $P(Y = 1 |\ \theta) = \theta$. And when $Y = 0$, this expression becomes $P(Y = 0\ |\ \theta) = 1 - \theta$.

This particular expression is actually the probability mass function (pmf) of the Bernoulli distribution. The Bernoulli distribution is used when we are describing a single trial (e.g. coin flip) with two possible outcomes (e.g. heads or tails). We can extend this to the situation where we have multiple independent trials very easily. First, we let $y_{i}$ represent the outcome of the ith coin flip and the set of all outcomes to be $D$. Since each coin toss is an independent trial (i.e. the outcome of a coin toss is independent of the previous coin toss outcomes), the probability of D is then multiplicative product of the individual outcomes:

\begin{align} P(D\ |\ \theta) &= \prod_{i}{P(y_{i}\ |\ \theta)} \\ P(D\ |\ \theta) & = \theta^{\#heads}(1 - \theta)^{\#tails} \end{align}

This expression is actually the probability mass function (pmf) for the binomial distribution. As we are dealing with multiple coin flips here, the binomial distribution serves as the perfect model for this data. As such, we can use the pmf of the binomial distribution to be our likelihood function for Bayes’ rule.

Cool Fact

The Bernoulli distribution is just a special case of the binomial distribution when there is only one trial.

Step 3: Specify Prior Distributions

Now that we have specified a probabilistic model to represent the coin toss, we have to consider the parameters of this model. The model is comprised of a single binomial distribution pmf which is parameterized by a single $\theta$. So we need to define some possible values that $\theta$ can take.

For this example, we will restrict our parameter values to discrete values of $\theta = 0, \theta = 0.1, …, \theta = 1.0$. And we believe that certain parameters are more likely. For instance, the probability of the coin being “fair” $\theta = 0.5$ is more likely than a coin being unfair. So we could define a probability distribution as follows:

This is called a “prior distribution” and defines the possibilities of $\theta$ and their associated probabilities.

Step 4: Collect Data and Application of Bayes’ Rule

The final step is that we use the observed data and apply Bayes’ rule to generate the posterior distribution. Let us start with the simplest scenario which is a single coin toss which has the outcome of a head. We now use Bayes’ rule to generate a posterior for each $\theta$ value:

Notice how the posterior probability distribution is different from the prior distribution. Specifically, the probability mass has shifted to higher $\theta$ values. This makes sense since that the outcome was head suggesting that the coin favours heads. Hence, $\theta$ values supporting a bias towards head outcomes will be favoured and thus we see the mass of the posterior distribution shifting towards that direction.

Importantly to note is that despite all data supporting heads, high $\theta$ values in the posterior distribution (e.g. 0.9 and 1) do not have a lot of probability. This makes sense since a single data point should not skew our prior belief of the probability of a head. If we had more data points, we might change our beliefs. This intuition makes sense and illustrates a key concept in Bayesian inference:

Key Concept

The posterior is a compromise between the prior and likelihood. Specifically:
• When there are less data points, the distribution is skewed towards the prior distribution.
• When there are more data points, the distribution is skewed towards the likelihood distribution.

This compromise is best demonstrated with more data. Let us see what happens when we have 20 coin toss and with a total of 15 heads.

Notice in this situation how the mass has shifted even more to the right side and in particular the majority of it is on 0.7 and 0.8. This makes sense again because the data suggest that $\theta$ should be around 0.75. The reason why we do not see mass at 0.75 is because we restricted our parameter space to discrete values of $\theta = 0, \theta = 0.1, …, \theta = 1.0$. We can easily expand our parameter space to a larger “grid”:

With more data points, we note that the posterior starts to resemble the likelihood. Let us take this one more step with 1000 data points and 750 heads:

Here is the posterior is almost exactly the same as the likelihood.

It’s also a good idea to check that the posterior probability distribution is a proper probability distribution by checking that it sums/integrates to:

Conclusions

In this post, I have introduced how one can make use of Bayes’ rule to do Bayesian inference. Remember that the happens when we start to interpret the variables of Bayes’ rule as parameters ($\theta$) of a model and observed data ($D$):

\begin{align} P(X\ |\ Y) &= \frac{P(Y\ |\ X)\ P(X)}{P(Y)} \\ P(\theta\ |\ D) &= \frac{P(D\ |\ \theta)\ P(\theta)}{P(D)} \end{align}

I have provided an overview of the 4 key steps to any Bayesian inference, and how the posterior distribution represents a compromise between the prior and likelihood. In this example, the prior distribution was chosen arbitrary and we calculated the posterior distribution by exhaustively calculating each value. This was possible because our parameter space was small, but does not scale well to larger parameter spaces. In follow-up posts, I will discuss how we can use an analytical approach to solve this, which in turn depends on our selection of a prior distribution. Moreover, I will discuss why Bayesian statistics is difficult and how a class of methods called Markov chain Monte Carlo (MCMC) can help us deal with this!

• 2020-12-15: Removed superfluous code in the get_likelihood_df function. Thanks Ben for pointing this out.
• 2019-09-19: Fixed the calculation of the marginal probability by multiplying the likelihood by the prior. This results in a proper posterior probability distribution that integrates to 1. Thanks Jose Tabora for pointing out the original posterior probability distribution wasn’t proper.