# Probability Theory Review¶

### Preliminaries¶

• Goal
• Review of probability theory from a logical reasoning viewpoint (i.e., a Bayesian interpretation)
• Materials

### Example Problem: Disease Diagnosis¶

• [Question] Given a disease with prevalence of 1% and a test procedure with sensitivity ('true positive' rate) of 95% and specificity ('true negative' rate) of 85% , what is the chance that somebody who tests positive actually has the disease?
• [Solution] Use probabilistic inference, to be discussed in this lecture.

### Why Probability Theory?¶

• Probability theory (PT) is the theory of optimal processing of incomplete information (see Cox theorem), and as such provides a quantitative framework for drawing conclusions from a finite (read: incomplete) data set.
• Machine learning concerns drawing conclusions from (a finite set of) data and therefore PT is the optimal calculus for machine learning.
• In general, nearly all interesting questions in machine learning can be stated in the following form (a conditional probability): $$p(\text{whatever-we-want-to-know}\, | \,\text{whatever-we-do-know})$$
• For example:
• Predictions $$p(\,\text{future-observations}\,|\,\text{past-observations}\,)$$
• Classify a received data point $$p(\,x\text{-belongs-to-class-}k \,|\,x\,)$$
• Information theory ("theory of log-probability") provides a source coding view on machine learning that is consistent with probability theory (more in part-2).

### Probability Theory Notation¶

• Define an event $A$ as a statement, whose truth is contemplated by a person, e.g.,

$$A = \text{'it will rain tomorrow'}$$

• We write the denial of $A$, i.e. the event not-A, as $\bar{A}$.
##### probabilities¶
• For any event $A$, with background knowledge $I$, the conditional probability of $A$ given $I$, is written as $$p(A|I)$$
• The value of a probability is limited to $0 \le p(A|I) \le 1$.
• All probabilities are in principle conditional probabilities of the type $p(A|I)$, since there is always some background knowledge.
• Still, we often write $p(A)$ rather than $p(A|I)$ if the background knowledge $I$ is assumed to be obviously present. E.g., $p(A)$ rather than $p(\,A\,|\,\text{the-sun-comes-up-tomorrow}\,)$.
##### probabilities for random variable assignments¶
• Note that, if $X$ is a random variable, then the assignment $X=x$ (with $x$ a value) can be interpreted as an event.
• We often write $p(x)$ rather than $p(X=x)$ (hoping that the reader understands the context ;-)
• In an apparent effort to further abuse notational conventions, $p(X)$ often denotes the full distribution over random variable $X$, i.e., the distribution for all assignments for $X$.
##### compound events¶
• The joint probability that both $A$ and $B$ are true, given $I$ (a.k.a. conjunction) is written as $$p(A,B|I)$$
• $A$ and $B$ are said to be independent, given $I$, if (and only if) $$p(A,B|I) = p(A|I)\,p(B|I)$$
• The probability that either $A$ or $B$, or both $A$ and $B$, are true, given $I$ (a.k.a. disjunction) is written as $$p(A+B|I)$$

### Probability Theory Calculus¶

• Normalization. If you know that event $A$ given $I$ is true, then $p(A|I)=1$.
• Product rule. The conjuction of two events $A$ and $B$ with given background $I$ is given by $$p(A,B|I) = p(A|B,I)\,p(B|I) \,.$$
• If $A$ and $B$ are independent given $I$, then $p(A|B,I) = p(A|I)$.
• Sum rule. The disjunction for two events $A$ and $B$ given background $I$ is given by $$p(A+B|I) = p(A|I) + p(B|I) - p(A,B|I)\,.$$
• As a special case, it follows from the sum rule that $p(A|I) + p(\bar{A}|I) = 1$
• Note that the background information may not change, e.g., if $I^\prime \neq I$, then $$p(A+B|I^\prime) \neq p(A|I) + p(B|I) - p(A,B|I)\,.$$
• All legitimate probabilistic relations can be derived from the sum and product rules!
• The product and sum rules are also known as the axioms of probability theory, but in fact, under some mild conditions, they can be derived as the unique rules for rational reasoning under uncertainty (Cox theorem, 1946).

### Frequentist vs. Bayesian Interpretation of Probabilities¶

• In the frequentist interpretation, $p(A)$ relates to the relative frequency that $A$ would occur under repeated execution of an experiment.
• For instance, if the experiment is tossing a coin, then $p(\texttt{tail}) = 0.4$ means that in the limit of a large number of coin tosses, 40% of outcomes turn up as $\texttt{tail}$.
• In the Bayesian interpretation, $p(A)$ reflects the degree of belief that event $A$ is true. I.o.w., the probability is associated with a state-of-knowledge (usually held by a person).
• For instance, for the coin tossing experiment, $p(\texttt{tail}) = 0.4$ should be interpreted as the belief that there is a 40% chance that $\texttt{tail}$ comes up if the coin were tossed.
• Under the Bayesian interpretation, PT calculus (sum and product rules) extends boolean logic to rational reasoning with uncertainty.
• The Bayesian viewpoint is more generally applicable than the frequentist viewpoint, e.g., it is hard to apply the frequentist viewpoint to the event '$\texttt{it will rain tomorrow}$'.
• The Bayesian viewpoint is clearly favored in the machine learning community. (In this class, we also strongly favor the Bayesian interpretation).

### The Sum Rule and Marginalization¶

• We discussed that every inference problem in PT can be evaluated through the sum and product rules. Next, we present two useful corollaries: (1) Marginalization and (2) Bayes rule
• If $X$ and $Y$ are random variables over finite domains, than it follows from the sum rule that $$p(X) = \sum_Y p(X,Y) = \sum_Y p(X|Y) p(Y) \,.$$
• Note that this is just a generalized sum rule. In fact, Bishop (p.14) (and some other authors as well) calls this the sum rule.
• EXERCISE: Proof the generalized sum rule.
• Of course, in the continuous domain, the (generalized) sum rule becomes $$p(X)=\int p(X,Y) \,\mathrm{d}Y$$
• Integrating $Y$ out of a joint distribution is called marginalization and the result $p(X)$ is sometimes referred to as the marginal probability.

### The Product Rule and Bayes Rule¶

• Consider 2 variables $D$ and $\theta$; it follows symmetry arguments that $$p(D,\theta)=p(D|\theta)p(\theta)=p(\theta|D)p(D)$$ and hence that $$p(\theta|D) = \frac{p(D|\theta) p(\theta)}{p(D)}\,.$$
• This formula is called Bayes rule (or Bayes theorem). While Bayes rule is always true, a particularly useful application occurs when $D$ refers to an observed data set and $\theta$ is set of model parameters that relates to the data. In that case,

• the prior probability $p(\theta)$ represents our state-of-knowledge about proper values for $\theta$, before seeing the data $D$.
• the posterior probability $p(\theta|D)$ represents our state-of-knowledge about $\theta$ after we have seen the data.

$\Rightarrow$ Bayes rule tells us how to update our knowledge about model parameters when facing new data. Hence,

Bayes rule is the fundamental rule for machine learning!

### Bayes Rule Nomenclature¶

• Some nomenclature associated with Bayes rule: $$\underbrace{p(\theta | D)}_{\text{posterior}} = \frac{\overbrace{p(D|\theta)}^{\text{likelihood}} \times \overbrace{p(\theta)}^{\text{prior}}}{\underbrace{p(D)}_{\text{evidence}}}$$
• Note that the evidence (a.k.a. marginal likelihood) can be computed from the numerator through marginalization since $$p(D) = \int p(D,\theta) \,\mathrm{d}\theta = \int p(D|\theta)\,p(\theta) \,\mathrm{d}\theta$$
• Hence, likelihood and prior is sufficient to compute both the evidence and the posterior. To emphasize that point, Bayes rule is sometimes written as $$p(\theta|D)\,p(D) = p(D|\theta)\, p(\theta)$$
• For given $D$, the posterior probabilities of the parameters scale relatively against each other as $$p(\theta|D) \propto p(D|\theta) p(\theta)$$

$\Longrightarrow$ All that we can learn from the observed data is contained in the likelihood function $p(D|\theta)$. This is called the likelihood principle.

### The Likelihood Function vs the Sampling Distribution¶

• Consider a model $p(D|\theta)$, where $D$ relates to a data set and $\theta$ are model parameters.
• In general, $p(D|\theta)$ is just a function of the two variables $D$ and $\theta$. We distinguish two interpretations of this function, depending on which variable is observed (or given by other means).
• The sampling distribution (a.k.a. the data-generating distribution) $$p(D|\theta=\theta_0)$$ (a function of $D$ only) describes the probability distribution for data $D$, assuming that it is generated by the given model with parameters fixed at $\theta = \theta_0$.
• In a machine learning context, often the data is observed, and $\theta$ is the free variable. For given observations $D=D_0$, the likelihood function (which is a function only of the model parameters $\theta$) is defined as $$\mathrm{L}(\theta) \triangleq p(D=D_0|\theta)$$
• Note that $\mathrm{L}(\theta)$ is not a probability distribution for $\theta$ since in general $\sum_\theta \mathrm{L}(\theta) \neq 1$.
• Technically, it is more correct to speak about the likelihood of a model (or model parameters) than about the likelihood of an observed data set. (Why?)

#### CODE EXAMPLE¶

Consider the following simple model for the outcome (head or tail) of a biased coin toss with parameter $\theta \in [0,1]$:

\begin{align*} y &\in \{0,1\} \\ p(y|\theta) &\triangleq \theta^y (1-\theta)^{1-y}\\ \end{align*}

We can plot both the sampling distribution (i.e. $p(y|\theta=0.8)$) and the likelihood function (i.e. $L(\theta) = p(y=0|\theta)$).

In [1]:
using Reactive, Interact, PyPlot
p(y,θ) = θ.^y .* (1-θ).^(1-y)
f = figure()
@manipulate for y=false, θ=0:0.1:1; withfig(f) do
# Plot the sampling distribution
subplot(221); stem([0,1], p([0,1],θ));
title("Sampling distribution");
xlim([-0.5,1.5]); ylim([0,1]); xlabel("y"); ylabel("p(y|θ=$(θ))"); # Plot the likelihood function _θ = linspace(0.0, 1.0, 100) subplot(222); plot(_θ, p(convert(Float64,y), _θ)); title("Likelihood function"); xlabel("θ"); ylabel("L(θ) = p(y=$(convert(Float64,y))|θ)");
end
end

Out[1]:

The (discrete) sampling distribution is a valid probability distribution. However, the likelihood function $L(\theta)$ clearly isn't, since $\int_0^1 L(\theta) \mathrm{d}\theta \neq 1$.

### Probabilistic Inference¶

• Probabilistic inference refers to computing $$p(\,\text{whatever-we-want-to-know}\, | \,\text{whatever-we-already-know}\,)$$
• For example: \begin{align*} p(\,\text{Mr.S.-killed-Mrs.S.} \;&|\; \text{he-has-her-blood-on-his-shirt}\,) \\ p(\,\text{transmitted-codeword} \;&|\;\text{received-codeword}\,) \end{align*}
• This can be accomplished by repeated application of sum and product rules.
• For instance, consider a joint distribution $p(X,Y,Z)$. Assume we are interested in $p(X|Z)$: \begin{align*} p(X|Z) \stackrel{p}{=} \frac{p(X,Z)}{p(Z)} \stackrel{s}{=} \frac{\sum_Y p(X,Y,Z)}{\sum_{X,Y} p(X,Y,Z)} \,, \end{align*} where the 's' and 'p' above the equality sign indicate whether the sum or product rule was used.
• In the rest of this course, we'll encounter many long probabilistic derivations. For each manipulation, you should be able to associate an 's' (for sum rule), a 'p' (for product or Bayes rule) or an 'a' (for a model assumption) above any equality sign. If you can't do that, file a github issue :)

### Working out the example problem: Disease Diagnosis¶

• [Question] - Given a disease $D$ with prevalence of $1\%$ and a test procedure $T$ with sensitivity ('true positive' rate) of $95\%$ and specificity ('true negative' rate) of $85\%$, what is the chance that somebody who tests positive actually has the disease?
• [Answer] - The given data are $p(D=1)=0.01$, $p(T=1|D=1)=0.95$ and $p(T=0|D=0)=0.85$. Then according to Bayes rule,

\begin{align*} p( D=1 &| T=1) \\ &= \frac{p(T=1|D=1)p(D=1)}{p(T=1)} \\ &= \frac{p(T=1|D=1)p(D=1)}{p(T=1|D=1)p(D=1)+p(T=1|D=0)p(D=0)} \\ &= \frac{0.95\times0.01}{0.95\times0.01 + 0.15\times0.99} = 0.0601 \end{align*}

### Inference Exercise: Bag Counter¶

• [Question] - A bag contains one ball, known to be either white or black. A white ball is put in, the bag is shaken, and a ball is drawn out, which proves to be white. What is now the chance of drawing a white ball?
• [Answer] - Again, use Bayes and marginalization to arrive at $p(\text{white}|\text{data})=2/3$, see homework exercise

• $\Rightarrow$ Note that probabilities describe a person's state of knowledge rather than a 'property of nature'.

• [Excercise] - Is a speech signal a 'probabilistic' (random) or a deterministic signal?

### Inference Exercise: Causality?¶

• [Question] - A dark bag contains five red balls and seven green ones. (a) What is the probability of drawing a red ball on the first draw? Balls are not returned to the bag after each draw. (b) If you know that on the second draw the ball was a green one, what is now the probability of drawing a red ball on the first draw?
• [Answer] - (a) $5/12$. (b) $5/11$, see homework.

• $\Rightarrow$ Again, we conclude that conditional probabilities reflect implications for a state of knowledge rather than temporal causality.

### PDF for the Sum of Two Variables¶

• [Question] - Given two random independent variables $X$ and $Y$, with PDF's $p_x(x)$ and $p_y(y)$. What is the PDF of $$Z = X + Y\;?$$
• [Answer] - Let $p_z(z)$ be the probability that $Z$ has value $z$. This occurs if $X$ has some value $x$ and at the same time $Y=z-x$, with joint probability $p_x(x)p_y(z-x)$. Since $x$ can be any value, we sum over all possible values for $x$ to get $$p_z (z) = \int_{ - \infty }^\infty {p_x (x)p_y (z - x)\,\mathrm{d}{x}}$$
• Iow, $p_z(z)$ is the convolution of $p_x$ and $p_y$.
• Note that $p_z(z) \neq p_x(x) + p_y(y)\,$ !!
• $\Rightarrow$ In linear stochastic systems theory, the Fourier Transform of a PDF (i.e., the characteristic function) plays an important computational role.
• This list shows how these convolutions work out for a few common probability distributions.

#### CODE EXAMPLE¶

• Consider the PDF of the sum of two independent Gaussians $X$ and $Y$:

\begin{align*} p_X(x) &= \mathcal{N}(\,x\,|\,\mu_X,\sigma_X^2\,) \\ p_Y(y) &= \mathcal{N}(\,y\,|\,\mu_Y,\sigma_Y^2\,) \\ Z &= X + Y \end{align*}

• Performing the convolution (nice exercise) yields a Gaussian PDF for $Z$:

$$p_Z(z) = \mathcal{N}(\,z\,|\,\mu_X+\mu_Y,\sigma_X^2+\sigma_Y^2\,).$$

In [2]:
using Reactive, Interact, PyPlot, Distributions
f = figure()
@manipulate for μx=-2:0.1:2, σx=0.1:0.1:1.9,μy=0:0.1:4, σy=0.1:0.1:0.9; withfig(f) do
μz = μx+μy; σz = sqrt(σx^2 + σy^2)
x = Normal(μx, σx)
y = Normal(μy, σy)
z = Normal(μz, σz)
range_min = minimum([μx-2*σx, μy-2*σy, μz-2*σz])
range_max = maximum([μx+2*σx, μy+2*σy, μz+2*σz])
range = linspace(range_min, range_max, 100)
plot(range, pdf.(x,range), "k-")
plot(range, pdf.(y,range), "b-")
plot(range, pdf.(z,range), "r-")
legend([L"p_X", L"p_Y", L"p_Z"])
grid()
end
end

Out[2]:

### Expectation and Variance¶

• The expected value or mean is defined as $$\mathrm{E}[f] \triangleq \int f(x) \,p(x) \,\mathrm{d}{x}$$
• The variance is defined as $$\mathrm{var}[f] \triangleq \mathrm{E} \left[(f(x)-\mathrm{E}[f(x)])^2 \right]$$
• The covariance matrix between vectors $x$ and $y$ is defined as \begin{align*} \mathrm{cov}[x,y] &\triangleq \mathrm{E}\left[ (x-\mathrm{E}[x]) (y^T-\mathrm{E}[y^T]) \right]\\ &= \mathrm{E}[x y^T] - \mathrm{E}[x]\mathrm{E}[y^T] \end{align*}
• Also useful as: $\mathrm{E}[xy^T] = \mathrm{cov}[x,y] + \mathrm{E}[x]\mathrm{E}[y^T]$

### Example: Mean and Variance for the Sum of Two Variables¶

• For any distribution of $x$ and $y$ and $z=x+y$,

\begin{align*} \mathrm{E}[z] &= \int_z z \left[\int_x p_x(x)p_y(z-x) \,\mathrm{d}{x} \right] \,\mathrm{d}{z} \\ &= \int_x p_x(x) \left[ \int_z z p_y(z-x)\,\mathrm{d}{z} \right] \,\mathrm{d}{x} \\ &= \int_x p_x(x) \left[ \int_{y^\prime} (y^\prime +x)p_y(y^\prime)\,\mathrm{d}{y^\prime} \right] \,\mathrm{d}{x} \notag\\ &= \int_x p_x(x) \left( \mathrm{E}[y]+x \right) \,\mathrm{d}{x} \notag\\ &= \mathrm{E}[x] + \mathrm{E}[y] \qquad \text{(always; follows from SRG-3a)} \end{align*}

• Derive as an exercise that

\begin{align*} \mathrm{var}[z] &= \mathrm{var}[x] + \mathrm{var}[y] + 2\mathrm{cov}[x,y] \qquad \text{(always, see SRG-3b)} \notag\\ &= \mathrm{var}[x] + \mathrm{var}[y] \qquad \text{(if X and Y are independent)} \end{align*}

### Linear Transformations¶

No matter how $x$ is distributed, we can easily derive that (do as exercise)

\begin{align} \mathrm{E}[Ax +b] &= A\mathrm{E}[x] + b \tag{SRG-3a}\\ \mathrm{cov}[Ax +b] &= A\,\mathrm{cov}[x]\,A^T \tag{SRG-3b} \end{align}

• (The tag (SRG-3a) refers to the corresponding eqn number in Sam Roweis' Gaussian Identities notes.)

### Review Probability Theory¶

• Interpretation as a degree of belief, i.e. a state-of-knowledge, not as a property of nature.
• We can do everything with only the sum rule and the product rule. In practice, Bayes rule and marginalization are often very useful for computing

$$p(\,\text{what-we-want-to-know}\,|\,\text{what-we-already-know}\,)\,.$$

• Bayes rule $$p(\theta|D) = \frac{p(D|\theta)p(\theta)} {p(D)}$$ is the fundamental rule for learning!
• That's really about all you need to know about probability theory, but you need to really know it, so do the exercises.

The cell below loads the style file

In [3]:
open("../../styles/aipstyle.html") do f