# Probability and statistics¶

In some form or another, machine learning is all about making
predictions. We might want to predict the *probability* of a patient
suffering a heart attack in the next year, given their clinical history.
In anomaly detection, we might want to assess how *likely* a set of
readings from an airplane’s jet engine would be, were it operating
normally. In reinforcement learning, we want an agent to act
intelligently in an environment. This means we need to think about the
probability of getting a high reward under each of the available action.
And when we build recommender systems we also need to think about
probability. For example, if we *hypothetically* worked for a large
online bookseller, we might want to estimate the probability that a
particular user would buy a particular book, if prompted. For this we
need to use the language of probability and statistics. Entire courses,
majors, theses, careers, and even departments, are devoted to
probability. So our goal here isn’t to teach the whole subject. Instead
we hope to get you off the ground, to teach you just enough that you
know everything necessary to start building your first machine learning
models and to have enough of a flavor for the subject that you can begin
to explore it on your own if you wish.

We’ve talked a lot about probabilities so far without articulating what precisely they are or giving a concrete example. Let’s get more serious by considering the problem of distinguishing cats and dogs based on photographs. This might sound simpler but it’s actually a formidable challenge. To start with, the difficulty of the problem may depend on the resolution of the image.

10px | 20px | 40px | 80px | 160px |
---|---|---|---|---|

While it’s easy for humans to recognize cats and dogs at 320 pixel
resolution, it becomes challenging at 40 pixels and next to impossible
at 10 pixels. In other words, our ability to tell cats and dogs apart at
a large distance (and thus low resolution) might approach uninformed
guessing. Probability gives us a formal way of reasoning about our level
of certainty. If we are completely sure that the image depicts a cat, we
say that the *probability* that the corresponding label \(l\) is
\(\mathrm{cat}\), denoted \(P(l=\mathrm{cat})\) equals 1.0. If
we had no evidence to suggest that \(l =\mathrm{cat}\) or that
\(l = \mathrm{dog}\), then we might say that the two possibilities
were equally \(likely\) expressing this as
\(P(l=\mathrm{cat}) = 0.5\). If we were reasonably confident, but
not sure that the image depicted a cat, we might assign a probability
\(.5 < P(l=\mathrm{cat}) < 1.0\).

Now consider a second case: given some weather monitoring data, we want to predict the probability that it will rain in Taipei tomorrow. If it’s summertime, the rain might come with probability \(.5\) In both cases, we have some value of interest. And in both cases we are uncertain about the outcome. But there’s a key difference between the two cases. In this first case, the image is in fact either a dog or a cat, we just don’t know which. In the second case, the outcome may actually be a random event, if you believe in such things (and most physicists do). So probability is a flexible language for reasoning about our level of certainty, and it can be applied effectively in a broad set of contexts.

## Basic probability theory¶

Say that we cast a die and want to know what the chance is of seeing a \(1\) rather than another digit. If the die is fair, all six outcomes \(\mathcal{X} = \{1, \ldots, 6\}\) are equally likely to occur, hence we would see a \(1\) in \(1\) out of \(6\) cases. Formally we state that \(1\) occurs with probability \(\frac{1}{6}\).

For a real die that we receive from a factory, we might not know those proportions and we would need to check whether it is tainted. The only way to investigate the die is by casting it many times and recording the outcomes. For each cast of the die, we’ll observe a value \(\{1, 2, \ldots, 6\}\). Given these outcomes, we want to investigate the probability of observing each outcome.

One natural approach for each value is to take the individual count for
that value and to divide it by the total number of tosses. This gives us
an *estimate* of the probability of a given event. The law of large
numbers tell us that as the number of tosses grows this estimate will
draw closer and closer to the true underlying probability. Before going
into the details of what’s going here, let’s try it out.

To start, let’s import the necessary packages:

```
In [1]:
```

```
import mxnet as mx
from mxnet import nd
```

Next, we’ll want to be able to cast the die. In statistics we call this
process of drawing examples from probability distributions *sampling*.
The distribution which assigns probabilities to a number of discrete
choices is called the *multinomial* distribution. We’ll give a more
formal definition of *distribution* later, but at a high level, think of
it as just an assignment of probabilities to events. In MXNet, we can
sample from the multinomial distribution via the aptly named
`nd.random.multinomial`

function. The function can be called in many
ways, but we’ll focus on the simplest. To draw a single sample, we
simply give pass in a vector of probabilities.

```
In [2]:
```

```
probabilities = nd.ones(6) / 6
nd.random.multinomial(probabilities)
```

```
Out[2]:
```

```
[3]
<NDArray 1 @cpu(0)>
```

If you run the sampler a bunch of times, you’ll find that you get out
random values each time. As with estimating the fairness of a die, we
often want to generate many samples from the same distribution. It would
be really slow to do this with a Python `for`

loop, so
`random.multinomial`

supports drawing multiple samples at once,
returning an array of independent samples in any shape we might desire.

```
In [3]:
```

```
print(nd.random.multinomial(probabilities, shape=(10)))
print(nd.random.multinomial(probabilities, shape=(5,10)))
```

```
[3 4 5 3 5 3 5 2 3 3]
<NDArray 10 @cpu(0)>
[[2 2 1 5 0 5 1 2 2 4]
[4 3 2 3 2 5 5 0 2 0]
[3 0 2 4 5 4 0 5 5 5]
[2 4 4 2 3 4 4 0 4 3]
[3 0 3 5 4 3 0 2 2 1]]
<NDArray 5x10 @cpu(0)>
```

Now that we know how to sample rolls of a die, we can simulate 1000 rolls. We can then go through and count, after each of the 1000 rolls, how many times each number was rolled.

```
In [4]:
```

```
rolls = nd.random.multinomial(probabilities, shape=(1000))
counts = nd.zeros((6,1000))
totals = nd.zeros(6)
for i, roll in enumerate(rolls):
totals[int(roll.asscalar())] += 1
counts[:, i] = totals
```

To start, we can inspect the final tally at the end of \(1000\) rolls.

```
In [5]:
```

```
totals / 1000
```

```
Out[5]:
```

```
[0.167 0.168 0.175 0.159 0.158 0.173]
<NDArray 6 @cpu(0)>
```

As you can see, the lowest estimated probability for any of the numbers is about \(.15\) and the highest estimated probability is \(0.188\). Because we generated the data from a fair die, we know that each number actually has probability of \(1/6\), roughly \(.167\), so these estimates are pretty good. We can also visualize how these probabilities converge over time towards reasonable estimates.

To start let’s take a look at the `counts`

array which has shape
`(6, 1000)`

. For each time step (out of 1000), counts, says how many
times each of the numbers has shown up. So we can normalize each
\(j\)-th column of the counts vector by the number of tosses to give
the `current`

estimated probabilities at that time. The counts object
looks like this:

```
In [6]:
```

```
counts
```

```
Out[6]:
```

```
[[ 0. 0. 0. ... 165. 166. 167.]
[ 1. 1. 1. ... 168. 168. 168.]
[ 0. 0. 0. ... 175. 175. 175.]
[ 0. 0. 0. ... 159. 159. 159.]
[ 0. 1. 2. ... 158. 158. 158.]
[ 0. 0. 0. ... 173. 173. 173.]]
<NDArray 6x1000 @cpu(0)>
```

Normalizing by the number of tosses, we get:

```
In [7]:
```

```
x = nd.arange(1000).reshape((1,1000)) + 1
estimates = counts / x
print(estimates[:,0])
print(estimates[:,1])
print(estimates[:,100])
```

```
[0. 1. 0. 0. 0. 0.]
<NDArray 6 @cpu(0)>
[0. 0.5 0. 0. 0.5 0. ]
<NDArray 6 @cpu(0)>
[0.1980198 0.15841584 0.17821783 0.18811882 0.12871288 0.14851485]
<NDArray 6 @cpu(0)>
```

As you can see, after the first toss of the die, we get the extreme
estimate that one of the numbers will be rolled with probability
\(1.0\) and that the others have probability \(0\). After
\(100\) rolls, things already look a bit more reasonable. We can
visualize this convergence by using the plotting package `matplotlib`

.
If you don’t have it installed, now would be a good time to install
it.

```
In [8]:
```

```
%matplotlib inline
from matplotlib import pyplot as plt
from IPython import display
display.set_matplotlib_formats('svg')
plt.figure(figsize=(8, 6))
for i in range(6):
plt.plot(estimates[i, :].asnumpy(), label=("P(die=" + str(i) +")"))
plt.axhline(y=0.16666, color='black', linestyle='dashed')
plt.legend()
plt.show()
```

Each solid curve corresponds to one of the six values of the die and gives our estimated probability that the die turns up that value as assessed after each of the 1000 turns. The dashed black line gives the true underlying probability. As we get more data, the solid curves converge towards the true answer.

In our example of casting a die, we introduced the notion of a **random
variable**. A random variable, which we denote here as \(X\) can be
pretty much any quantity and is not determistic. Random variables could
take one value among a set of possibilites. We denote sets with
brackets, e.g., \(\{\mathrm{cat}, \mathrm{dog}, \mathrm{rabbit}\}\).
The items contained in the set are called *elements*, and we can say
that an element \(x\) is *in* the set S, by writing \(x \in S\).
The symbol \(\in\) is read as “in” and denotes membership. For
instance, we could truthfully say
\(\mathrm{dog} \in \{\mathrm{cat}, \mathrm{dog}, \mathrm{rabbit}\}\).
When dealing with the rolls of die, we are concerned with a variable
\(X \in \{1, 2, 3, 4, 5, 6\}\).

Note that there is a subtle difference between discrete random
variables, like the sides of a dice, and continuous ones, like the
weight and the height of a person. There’s little point in asking
whether two people have exactly the same height. If we take precise
enough measurements you’ll find that no two people on the planet have
the exact same height. In fact, if we take a fine enough measurement,
you will not have the same height when you wake up and when you go to
sleep. So there’s no purpose in asking about the probability that
someone is \(2.00139278291028719210196740527486202\) meters tall.
Given the world population of humans the probability is virtually 0. It
makes more sense in this case to ask whether someone’s height falls into
a given interval, say between 1.99 and 2.01 meters. In these cases we
quantify the likelihood that we see a value as a *density*. The height
of exactly 2.0 meters has no probability, but nonzero density. In the
interval between any two different heights we have nonzero probability.

There are a few important axioms of probability that you’ll want to remember:

- For any event \(z\), the probability is never negative, i.e. \(\Pr(Z=z) \geq 0\).
- For any two events \(Z=z\) and \(X=x\) the union is no more likely than the sum of the individual events, i.e. \(\Pr(Z=z \cup X=x) \leq \Pr(Z=z) + \Pr(X=x)\).
- For any random variable, the probabilities of all the values it can take must sum to 1, i.e. \(\sum_{i=1}^n P(Z=z_i) = 1\).
- For any two mutually exclusive events \(Z=z\) and \(X=x\), the probability that either happens is equal to the sum of their individual probabilities that \(\Pr(Z=z \cup X=x) = \Pr(Z=z) + \Pr(X=z)\).

## Dealing with multiple random variables¶

Very often, we’ll want consider more than one random variable at a time. For instance, we may want to model the relationship between diseases and symptoms. Given a disease and symptom, say ‘flu’ and ‘cough’, either may or may not occur in a patient with some probability. While we hope that the probability of both would be close to zero, we may want to estimate these probabilities and their relationships to each other so that we may apply our inferences to effect better medical care.

As a more complicated example, images contain millions of pixels, thus millions of random variables. And in many cases images will come with a label, identifying objects in the image. We can also think of the label as a random variable. We can even get crazy and think of all the metadata as random variables such as location, time, aperture, focal length, ISO, focus distance, camera type, etc. All of these are random variables that occur jointly. When we deal with multiple random variables, there are several quantities of interest. The first is called the joint distribution \(\Pr(A, B)\). Given any elements \(a\) and \(b\), the joint distribution lets us answer, what is the probability that \(A=a\) and \(B=b\) simulataneously? It might be clear that for any values \(a\) and \(b\), \(\Pr(A,B) \leq \Pr(A=a)\).

This has to be the case, since for \(A\) and \(B\) to happen,
\(A\) has to happen *and* \(B\) also has to happen (and vice
versa). Thus \(A,B\) cannot be more likely than \(A\) or
\(B\) individually. This brings us to an interesting ratio:
\(0 \leq \frac{\Pr(A,B)}{\Pr(A)} \leq 1\). We call this a
**conditional probability** and denote it by \(\Pr(B | A)\), the
probability that \(B\) happens, provided that \(A\) has
happened.

Using the definition of conditional probabilities, we can derive one of the most useful and celebrated equations in statistics - Bayes’ theorem. It goes as follows: By construction, we have that \(\Pr(A, B) = \Pr(B | A) \Pr(A)\). By symmetry, this also holds for \(\Pr(A,B) = \Pr(A | B) \Pr(B)\). Solving for one of the conditional variables we get:

This is very useful if we want to infer one thing from another, say
cause and effect but we only know the properties in the reverse
direction. One important operation that we need to make this work is
**marginalization**, i.e., the operation of determining \(\Pr(A)\)
and \(\Pr(B)\) from \(\Pr(A,B)\). We can see that the
probability of seeing \(A\) amounts to accounting for all possible
choices of \(B\) and aggregating the joint probabilities over all of
them, i.e.

A really useful property to check is for **dependence** and
**independence**. Independence is when the occurrence of one event does
not influence the occurrence of the other. In this case
\(\Pr(B | A) = \Pr(B)\). Statisticians typically use
\(A \perp\!\!\!\perp B\) to express this. From Bayes Theorem it
follows immediately that also \(\Pr(A | B) = \Pr(A)\). In all other
cases we call \(A\) and \(B\) dependent. For instance, two
successive rolls of a dice are independent. On the other hand, the
position of a light switch and the brightness in the room are not (they
are not perfectly deterministic, though, since we could always have a
broken lightbulb, power failure, or a broken switch).

Let’s put our skills to the test. Assume that a doctor administers an AIDS test to a patient. This test is fairly accurate and it fails only with 1% probability if the patient is healthy by reporting him as diseased. Moreover, it never fails to detect HIV if the patient actually has it. We use \(D\) to indicate the diagnosis and \(H\) to denote the HIV status. Written as a table the outcome \(\Pr(D | H)\) looks as follows:

outcome | HIV positive | HIV negative |
---|---|---|

Test positive | 1 | 0.01 |

Test negative | 0 | 0.99 |

Note that the column sums are all one (but the row sums aren’t), since the conditional probability needs to sum up to \(1\), just like the probability. Let us work out the probability of the patient having AIDS if the test comes back positive. Obviously this is going to depend on how common the disease is, since it affects the number of false alarms. Assume that the population is quite healthy, e.g. \(\Pr(\text{HIV positive}) = 0.0015\). To apply Bayes Theorem we need to determine

Hence we get

In other words, there’s only a 13.1% chance that the patient actually has AIDS, despite using a test that is 99% accurate! As we can see, statistics can be quite counterintuitive.

## Conditional independence¶

What should a patient do upon receiving such terrifying news? Likely, he/she would ask the physician to administer another test to get clarity. The second test has different characteristics (it isn’t as good as the first one).

outcome | HIV positive | HIV negative |
---|---|---|

Test positive | 0.98 | 0.03 |

Test negative | 0.02 | 0.97 |

Unfortunately, the second test comes back positive, too. Let us work out the requisite probabilities to invoke Bayes’ Theorem.

- \(\Pr(D_1 = 1 \text{ and } D_2 = 1 | H = 0) = 0.01 \cdot 0.03 = 0.0003\)
- \(\Pr(D_1 = 1 \text{ and } D_2 = 1 | H = 1) = 1 \cdot 0.98 = 0.98\)
- \(\Pr(D_1 = 1 \text{ and } D_2 = 1) = 0.0001 \cdot 0.9985 + 0.98 \cdot 0.0015 = 0.00176955\)
- \(\Pr(H = 1 | D_1 = 1 \text{ and } D_2 = 1) = \frac{0.98 \cdot 0.0015}{0.00176955} = 0.831\)

That is, the second test allowed us to gain much higher confidence that
not all is well. Despite the second test being considerably less
accurate than the first one, it still improved our estimate quite a bit.
*Why couldn’t we just run the first test a second time?* After all, the
first test was more accurate. The reason is that we needed a second test
that confirmed *independently* of the first test that things were dire,
indeed. In other words, we made the tacit assumption that
\(\Pr(D_1, D_2 | H) = \Pr(D_1 | H) \Pr(D_2 | H)\). Statisticians
call such random variables **conditionally independent**. This is
expressed as \(D_1 \perp\!\!\!\perp D_2 | H\).

## Summary¶

So far we covered probabilities, independence, conditional independence, and how to use this to draw some basic conclusions. This is already quite powerful. In the next section we will see how this can be used to perform some basic estimation using a Naive Bayes classifier.

## Problems¶

- Given two events with probability \(\Pr(A)\) and \(\Pr(B)\), compute upper and lower bounds on \(\Pr(A \cup B)\) and \(\Pr(A \cap B)\). Hint - display the situation using a Venn Diagram.
- Assume that we have a sequence of events, say \(A\), \(B\) and \(C\), where \(B\) only depends on \(A\) and \(C\) only on \(B\), can you simplify the joint probability? Hint - this is a Markov Chain.