Showing posts with label data science. Show all posts
Showing posts with label data science. Show all posts

# What is Bayes' theorem and why is it so important?

Bayes' theorem is one of the key ideas of modern data science; it's enabling more accurate forecasting, it's leading to shorter A/B tests, and it's fundamentally changing statistical practices. In the last twenty years, Bayes' theorem has gone from being a cute probability idea to becoming central to many disciplines. Despite its huge impact, it's a simple statement of probabilities: what is the probability of an event occurring given some other event has occurred? How can something almost trivial be so revolutionary? Why all this change now? In this blog post, I'm going to give you a brief introduction to Bayes' theorem and show you why it's so powerful.

(Bayes theorem. Source: Wikimedia Commons. Author: Matt Buck. License: Creative Commons.)

# A disease example without explicitly using Bayes' theorem

To get going, I want to give you a motivating example that shows you the need for Bayes' theorem. I'm using this problem to introduce the language we'll need. I'll be using basic probability theory to solve this problem and you can find all the theory you need in my previous blog post on probability. This example is adapted from Wayne W. LaMorte's page at BU; he has some great material on probability and it's well worth your time browsing his pages.

Imagine there's a town of 10,000 people. 1% of the town's population has a disease. Fortunately, there's a very good test for the disease:

• If you have the disease, the test will give a positive result 99% of the time (sensitivity).
• If you don't have the disease, the test will give a negative result 99% of the time (specificity).

You go into the clinic one day and take the test. You get a positive result. What's the probability you have the disease? Before you go on, think about your answer and the why behind it.

• D+ and D- represent having the disease and not having the disease
• T+ and T- represent testing positive and testing negative
• P(D+) represents the probability of having the disease (with similar meanings for P(D-), P(T+), P(T-))
• P(T+ | D+) is the probability of testing positive given that you have the disease.

We can write out what we know so far:

• P(D+) = 0.01
• P(T+ | D+) = 0.99
• P(T- | D-) = 0.99

We want to know P(D+ | T+). I'm going to build a decision tree to calculate what I need.

There are 10,000 people in the town, and 1% of them have the disease. We can draw this in a tree diagram like so.

For each of the branches, D+ and D-, we can draw branches that show the test results T+ and T-:

For example, we know 100 people have the disease, of whom 99% will test positive, which means 1% will test negative. Similarly, for those who do not have the disease, (9,900), 99% will test negative (9,801), and 1% will test positive (99).

Out of 198 people who tested positive for the disease (P(T+) = P(T+ | D+) + P(T+ | D-)), 99 people have it, so P(D+ | T+) = 99/198. In other words, if I test positive for the disease, I have a 50% chance of actually having it.

There are two takeaways from all of this:

• Wow! Really, only a 50% probability! I thought it would be much higher! (This is called the base rate fallacy).
• This is a really tedious process and probably doesn't scale. Can we do better? (Yes: Bayes' theorem.)

# Who was Bayes?

Thomas Bayes (1702-1761), was an English non-conformist minister (meaning a protestant minister not part of the established Church of England). His religious duties left him time for mathematical exploration, which he did for his own pleasure and amusement; he never published in his lifetime in his own name. After his death, his friend and executor, Richard Price, went through his papers and found an interesting result, which we now call Bayes' theorem.  Price presented it at the Royal Society and the result was shared with the mathematical community.

(Plaque commemorating Thomas Bayes. Source: Wikimedia Commons Author:Simon Harriyott License: Creative Commons.)

For those of you who live in London, or visit London, you can visit the Thomas Bayes memorial in the historic Bunhill Cemetery where Bayes is buried. For the true probability pilgrim, it might also be worth visiting Richard Price's grave which is only a short distance away.

# Bayes' theorem

The derivation of Bayes' theorem is almost trivial. From basic probability theory:

$P(A \cap B) = P(A) P(B | A)$
$P(A \cap B) = P(B \cap A)$

With some re-arranging we get the infamous theorem:

$P(A | B) = \frac{P(B | A) P(A)}{P(B)}$

Although this is the most compact version of the theorem, it's more usefully written as:

$P(A | B) = \frac{P(B | A) P(A)}{P(B \cap A) + P(B \cap \bar A)} = \frac{P(B | A)P(A)}{P(B | A)P(A) + P(B | \bar A) P( \bar A)}$

where $$\bar A$$ means not A (remember $$1 = P(A) + P(\bar A)$$). You can get this second form of Bayes using the law of total probability and the multiplication rule (see my previous blog post).

So what does it all mean and why is there so much excitement over something so trivial?

# What does Bayes' theorem mean?

The core idea of Bayesian statistics is that we update our prior beliefs as new data becomes available - we go from the prior to the posterior. This process is often iterative and is called the diachronic interpretation of Bayes theorem. It usually requires some computation; something that's reasonable to do given today's computing power and the free availability of numeric computing languages. This form of Bayes is often written:

$P(H | D) = \frac{P(D | H) P(H)}{P(D)}$

with these definitions:

• P(H) - the probability of the hypothesis before the new data - often called the prior
• P(H | D) - the probability of the hypothesis after the data - the posterior
• P(D | H) - the probability of the data under the hypothesis, the likelihood
• P(D) - the probability of the data, it's called the normalizing constant

A good example of the use of Bayes' theorem is its use to better quantify the health risk an individual faces from a disease. Let's say the risk of suffering a heart attack in any year is P(HA), however, this is for the population as a whole (the prior). If someone smokes, the probability becomes P(HA | S), which is the posterior, which may be considerably different from P(HA).

Let's use some examples to figure out how Bayes works in practice.

# The disease example using Bayes

Let's start from this version of Bayes:

$P(A | B) = \frac{P(B | A)P(A)}{P(B | A)P(A) + P(B | \bar A) P( \bar A)}$

and use the notation from our disease example:

$P(D+ | T+) = \frac{P(T+ | D+)P(D+)}{P(T+ | D+)P(D+) + P(T+ | D-) P( D-)}$

Here's what we know from our previous disease example:

• P(D+) = 0.01 and by implication P(D-) = 0.99
• P(T+ | D+) = 0.99
• P(T- | D-) = 0.99 and by implication P(T+ | D-) = 0.01

Plugging in the numbers:

$P(D+ | T+) = \frac{0.99\times0.01}{0.99\times0.01 + 0.01\times0.99} = 0.5$

The decision tree is easier for a human to understand, but if there are a large number of conditions, it becomes much harder to use. For a computer on the other hand, the Bayes solution is straightforward to code and it's expandable for a large number of conditions.

# Predicting US presidential election results

• To predict a winner, you need to model the electoral college, which implies a state-by-state forecast.
• For each state, you know who won last time, so you have a prior in the Bayesian sense.
• In competitive states, there are a number of opinion polls that provide evidence of voter intention, this is the data or normalizing constant in Bayes-speak.

In practice, you start with a state-by-state prior based on previous elections or fundamentals, or something else. As opinion polls are published, you calculate a posterior probability for each of the parties to win the state election. Of course, you do this with Bayes theorem. As more polls come in, you update your model and the influence of your prior becomes less and less. In some versions of this type of modeling work, models take into account national polling trends too.

The landmark paper describing this type of modeling is by Linzer.

# Using Bayes' theorem to prove the existence of God

Over history, there have been many attempts to prove the existence of God using scientific or mathematical methods. All of them have floundered for one reason or another. Interestingly, one of the first uses of Bayes' theorem was to try and prove the existence of God by proving miracles can happen. The argument was put forward by Richard Price himself. I'm going to repeat his analysis using modern notation, based on an explanation from Cornell University.

Price's argument is based on tides. We expect tides to happen every day, but if a tide doesn't happen, that would be a miracle. If T is the consistency of tides, and M is a miracle (no tide), then we can use Bayes theorem as:

$P(M | T) = \frac{P(T | M) P(M)}{P(T | M) P(M) + P(T | \bar M) P(\bar M)}$

Price assumed the probability of miracles existing was the same as the probability of miracles not existing (!), so $$P(M) = P(\bar M)$$. If we plug this into the equation above and simplify, we get:

$P(M | T) = \frac{P(T | M)}{P(T | M) + P(T | \bar M)}$

He further assumed that if miracles exist, they would be very rare (or we would see them all the time), so:

$P(T | \bar M) >> P(T | M)$

he further assumed that $$P(T | M) = 1e^{-6}$$ - in other words, if a miracle exists, it would happen 1 time in 1 million. He also assumed that if there were no miracles, tides would always happen, so $$P(T | \bar M) = 1$$. The upshot of all this is that:

$P(M | T) = 0.000001$

or, there's a 1 in a million chance of a miracle happening.

There are more holes in this argument than in a teabag, but it is an interesting use of Bayes' theorem and does give you some indication of how it might be used to solve other problems.

# Monty Hall and Bayes

The Monty Hall problem has tripped people up for decades (see my previous post on the problem). Using Bayes' theorem, we can rigorously solve it.

Here's the problem. You're on a game show hosted by Monty Hall and your goal is to win the car. He shows you three doors and asks you to choose one. Behind two of the doors are goats and behind one of the doors is a car. Once you've chosen your door, Monty opens one of the other doors to show you what's behind it. He always chooses a door with a goat behind it. Next, he asks you the key question: "do you want to change doors?". Should you change doors and why?

I'm going to use the diachronic interpretation of Bayes theorem to figure out what happens if we don't change:

$P(H | D) = \frac{P(D | H) P(H)}{P(D)} = \frac{P(D | H) P(H)}{P(D | H)P(H) + P(D | \bar H) P( \bar D)}$
• $$P(H)$$ is the probability our initial choice of door has a car behind it, which is $$\frac{1}{3}$$.
• $$P( \bar H) = 1- P(H) = \frac{2}{3}$$
• $$P(D | H) = 1$$ this is the probability Monty will show me a door with a goat given that I have chosen the door with a car - it's always 1 because Monty always shows me the door with a goat
• $$P(D | \bar H) = 1$$ this is the probability Monty will show me a door with a goat given that I have chosen the door with a goat - it's always 1 because Monty always shows me the door with a goat,

Plugging these numbers in:

$P(H | D) = \frac{1 \times \frac{1}{3}}{1 \times \frac{1}{3} + 1 \times \frac{2}{3}} = \frac{1}{3}$

If we don't change, then the probability of winning is the same as if Monty hadn't opened the other door. But there are only two doors, and $$P(\bar H) + P(H) = 1$$. In turn, this means our winning probability if we switch is $$\frac{2}{3}$$, so our best strategy is switching.

# Searching for crashed planes and shipwrecks

On 1st June 2009, Air France Flight AF 447 crashed into the Atlantic. Although the flight had been tracked, the underwater search for the plane was complex. The initial search used Bayesian inference to try and locate where on the ocean floor the plane might be. It used data from previous crashes that assumed the underwater locator beacon was working. Sadly, the initial search didn't find the plane.

In 2011, a new team re-examined the data, with two crucial differences. Firstly, they had data from the first search, and secondly, they assumed the underwater locator beacon had failed. Again using Bayesian inference, they pointed to an area of ocean that had already been searched. The ocean was searched again (with the assumption the underwater beacon had failed), and this time the plane was found.

You can read more about this story in the MIT Technology Review and for more in-depth details, you can read the paper by the team that did the analysis.

It turns out, there's quite a long history of analysts using Bayes theorem to locate missing ships. In this 1971 paper, Richardson and Stone show how it was used to locate the wreckage of the USS Scorpion. Since then, a number of high-profile wrecks have been located using similar methods.

Sadly, even Bayes' theorem hasn't led to anyone finding flight MH370.

# Other examples of Bayes' theorem

Bayes has been applied in many, many disciplines. I'm not going to give you an exhaustive list, but I will give you some of the more 'fun' ones.

# Why now?

Using Bayes theorem can involve a lot of fairly tedious arithmetic. If the problem requires many iterations, there are lots of tedious calculations. This held up the adoption of Bayesian methods until three things happened:

• Cheap computing.
• The free and easy availability of mathematical computing languages.
• Widespread skill to program in these languages.

By the late 1980s, computing power was sufficiently cheap to make Bayesian methods viable, and of course, computing has only gotten cheaper since then. Good quality mathematical languages were available by the late 1980s too (e.g. Fortran, MATLAB), but by the 2010s, Python and R had all the necessary functionality and were freely and easily available. Both Python and R usage had been growing for a while, but by the 2010s, there was a very large pool of people who were fluent in them.

As they say in murder mysteries, by the 2010s, Bayesian methods had the means, the motive, and the opportunity.

# Bayes and the remaking of statistics

Traditional (non-Bayesian) statistics are usually called frequentist statistics. It has a long history and has been very successful, but it has problems. In the last 50 years, Bayesian analysis has become more successful and is now challenging frequentist statistics.

I'm not going to provide an in-depth critique of frequentist statistics here, but I will give you a high-level summary of some of the problems.

• p-values and significance levels are prone to misunderstandings - and the choice of significance levels is arbitrary
• Much of the language surrounding statistical tests is complex and rests on convention rather than underlying theory
• The null hypothesis test is frequently misunderstood and misinterpreted
• Prior information is mostly ignored.

Bayesian methods help put statistics on a firmer intellectual foundation, but the price is changing well-understood and working frequentist statistics. In my opinion, over the next twenty years, we'll see Bayesian methods filter down to undergraduate level and gradually replace the frequentist approach. But for right now, the frequentists rule.

# Conclusion

At its heart, Bayes' theorem is almost trivial, but it's come to represent a philosophy and approach to statistical analysis that modern computing has enabled; it's about updating your beliefs with new information. A welcome side-effect is that it's changing statistical practice and putting it on a firmer theoretical foundation. Widespread change to Bayesian methods will take time, however, especially because frequentist statistics are so successful.

# The summary is not the whole picture

If you just use summary statistics to describe your data, you can miss the bigger picture, sometimes literally so. In this blog post, I'm going to show you how relying on summaries alone can lead you catastrophically astray and I'm going to tell you how you can avoid making career-damaging mistakes.

The datasaurus is why you need to visualize your data. Source: Alberto Cairo. Open source.

# What are summary statistics?

Summary statistics are parameters like the mean, standard deviation, and correlation coefficient; they summarize the properties of the data and the relationship between variables. For example, if the correlation coefficient, r, is about 0.8 for two data sets x and y, we might think there's a relationship between them, but if it's about 0, we might think there isn't.

The use of summary statistics is widely taught, every textbook emphasizes them, and almost everyone uses them. But if you use summary statistics in isolation from other methods you might miss important relationships - you should always visualize your data as we'll see.

# Anscombe's Quartet

Take a look at the four plots below. They're obviously quite different, but they all have the same summary statistics!

Here are the summary statistics data:

PropertyValue
Mean of x9
Sample variance of x : ${\displaystyle \sigma ^{2}}$11
Mean of y7.50
Sample variance of y : ${\displaystyle \sigma ^{2}}$4.125
Correlation between x and y0.816
Linear regression liney = 3.00 + 0.500x
Coefficient of determination of the linear regression : ${\displaystyle R^{2}}$0.67

These plots were developed in 1973 by the statistician Francis Anscombe to make exactly this point: you can't rely on summary statistics, you need to visualize your data. The graphical relationship between the x and y variables is different in each case and implies different things. By plotting the data out, we can see what the relationships are, but summary statistics hide what's going on.

# The datasaurus

Let's zoom forward to 2016. The justly famous Alberto Cairo tweeted about Anscombe's quartet and illustrated the point with this cool set of summary statistics. He later expanded on his tweet in a short blog post.

Property Value
n 142
mean 54.2633
x standard deviation 16.7651
y mean 47.8323
y standard deviation 26.9353
Pearson correlation -0.0645

What might you conclude from these summary statistics? I might say, the correlation coefficient is close to zero so there's not much of a relationship between the x and the y variables. I might conclude there's no interesting relationship between the x and y variables - but I would be wrong.

The summary might not mean anything to you, but the visualization surely will. This is the datasaurus data set, the x and the y variables draw out a dinosaur.

# The datasaurus dozen

Two researchers at Autodesk Research took things a stage further. They started with Alberto Cairo's datasaurus and created a dozen other charts with exactly the same summary statistics as the datasaurus. Here they all are.

The summary statistics look like noise, but the charts reveal the underlying relationships between the x and y variables. Some of these relationships are obviously fun, like the star, but there are others that imply more meaningful relationships.

If all this sounds a bit abstract, let's think about how this might manifest itself in business. Let's imagine you're an analyst working for a large company. You have data on sales by store size for Europe and you've been asked to analyze the data to gain insights. You're under time pressure, so you fire up a Python notebook and get some quick summary statistics. You get summary statistics that look like the ones I showed you above. So you conclude there's nothing interesting in the data, but you might be very wrong.

You should plot the data out and look at the chart. You might see something that looks like the slanting charts above, maybe something like this:

the individual diagonal lines might correspond to different European countries (different regulations, different planning rules, different competition, etc.). There could be a very significant relationship that you would have missed by relying on summary data.

(The Autodesk Research team have posted their work as a paper you can read here.)

# Lessons learned

The lessons you should take away from all this are simple:

• summary statistics hide a lot
• there are many relationships between variables that will give summary statistics that look like noise

## Tuesday, March 24, 2020

### John Snow, cholera, and the origins of data science

The John Snow story is so well known, it borders on the cliched, but I discovered some twists and turns I hadn't known that shed new light on what happened and on how to interpret Snow's results. Snow's story isn't just a foundational story for epidemiology, it's a foundational story for data science too.

(Image credit: Cholera bacteria, CDC; Broad Street pump, Betsy Weber; John Snow, Wikipedia)

To very briefly summarize: John Snow was a nineteenth-century doctor with an interest in epidemiology and cholera. When cholera hit London in 1854, he played a pivotal role in understanding cholera in two quite different ways, both of which are early examples of data science practices.

The first way was his use of registry data recording the number of cholera deaths by London district. Snow was able to link the prevalence of deaths to the water company that supplied water to each district. The Southwark & Vauxhall water company sourced their water from a relatively polluted part of the river Thames, while the Lambeth water company took their water from a relatively unpolluted part of the Thames. As it turned out, there was a clear relationship between drinking water source and cholera deaths, with polluted water leading to more deaths.

This wasn't a randomized control trial, but was instead an early form of difference-in-difference analysis. Difference-in-difference analysis was popularized by Card and Krueger in the mid-1990's and is now widely used in econometrics and other disciplines. Notably, there are many difference-in-difference tutorials that use Snow's data set to teach the method.

I've reproduced one of Snow's key tables below, the most important piece is the summary at the bottom comparing deaths from cholera by water supply company. You can see the attraction of this dataset for data scientists, it's calling out for the use of groupby.

The second way is a more dramatic tale and guaranteed his continuing fame. In 1854, there was an outbreak of cholera in the Golden Square part of Soho in London. Right from the start, Snow suspected the water pump at Broad Street was the source of the infection. Snow conducted door-to-door inquiries, asking what people ate and drank. He was able to establish that people who drank water from the pump died at a much higher rate than those that did not. The authorities were desperate to stop the infection, and despite the controversial nature of Snow's work, they listened and took action; famously, they removed the pump handle and the cholera outbreak stopped.

Snow continued his analysis after the pump handle was removed and wrote up his results (along with the district study I mentioned above) in a book published in 1855. In the second edition of his book, he included his famous map, which became an iconic data visualization for data science.

Snow knew where the water pumps were and knew where deaths had occurred. He merged this data into a map-bar chart combination; he started with a street map of the Soho area and placed a bar for each death that occurred at an address. His map showed a concentration of deaths near the Broad Street pump.

I've reproduced a section of his map below. The Broad Street pump I've highlighted in red and you can see a high concentration of deaths nearby. There are two properties that suffered few deaths despite being near the pump, the workhouse and the brewery. I've highlighted the workhouse in green. Despite housing a large number of people, few died. The workhouse had its own water supply, entirely separate from the Broad Street pump. The brewery (highlighted in yellow) had no deaths either; they supplied their workers with free beer (made from boiled water).