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Hello and welcome to the class on Probabilistic Graphical Models.

Probabilistic Graphical Models are a bit of a mouthful, so before we define them,

let's first figure out what they might be used for.

So, one example application, which in fact is the one where probabilistic graphical

models, or PGMs as they're called, first made its way into computer science and

artificial intelligence, is that as medical diagnosis.

Consider a doctor who's faced with a patient.

The doctor has a fair amount of information at her disposal when she looks

up at a patient.

Predisposing factors, symptom, the results of various tests.

And from that, she's supposed to figure out what diseases the patient might have

and what the response to different treatments might be.

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A very different application where PGMs have also been used is that of image

segmentation.

Here, we might have an image such as this that has thousands or

even hundreds of thousands of pixels, and

what we'd like to do is we'd like to figure out what each pixel corresponds to.

For example, if we break up the image into these fairly larger

regions to have less stuff to reason about,

we want to take figure out which of these corresponds to grass, sky, cow, or horse.

What do these two problems have in common?

First, they have a very large number of variables that we have to reason about.

In the context of a doctor, it's all these predisposing factors,

test results, possible diseases, and so on.

And in the context of the image segmentation, it's the labels for

these different pixels or these larger regions called superpixels.

The second thing that these applications have in common is that fundamentally there

is going to be significant uncertainty about the right answer,

no matter how clever the algorithms that we design.

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So, probabilistic graphical models are a framework for

dealing with this kind of application.

So, let's first understand what each of

these words mean in the context of this framework.

So first, let's consider the word models.

So what is a model?

The model is a declarative representation of our understanding of the world.

So it is a representation within the computer that captures our understanding

of what these variables are and how they interact with each other.

And the fact that it's declarative means that the representation stands on its own,

which means that we can look into it and

make sense of it aside from any algorithm that we might choose to apply on.

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So, why is that important?

It's important because that same representation, that same model can then

be used in the context of one algorithm that answers any one kind of question.

Or other algorithms that might answer different kinds of questions or

the same question in more efficient ways, or

that make different trade-offs between accuracy and complicational cause.

The other advantage of having a stand alone model is that we

can separate out the construction of the model from the algorithms that

are used to reason over it.

So, we can construct methodologies that elicit these models from a human expert or

ones that learn it from historical data using statistical machine learning

techniques or a combination of the two.

And once again, the separation between the algorithm and the model and

the learning in the model allows us to tackle each of these problems separately.

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So, that was the word model, what about probabilistic?

The word probabilistic is in there because these models are designed to help us deal

with large amounts of uncertainty.

So uncertainty comes in many forms and for many different reasons.

So, first it comes because we just have partial knowledge of the state of

the world, for example the doctor doesn't get to measure every symptom or every test

result and she's certainly uncertain about the diseases that the patient has.

Uncertainty comes because of noisy observations.

So even when we get to observe certain things like blood pressure,

those observations are often subject to significant amounts of noise.

Uncertainty also comes in because of modeling limitations, so

we're going to have phenomena that are just not covered by our model.

All sorts of different obscure diseases for

example that might cause the same set of symptoms.

It's impossible for us to write down the model that is so

detailed that includes every possible contingency in every possible factor.

And so you're going to have uncertainty and

variability that is simply due to modelling limitations.

And finally, some people would argue that the world is inherently stochastic.

Certainly, if you go down to the quantum level, that's true.

But even at a higher level, the modeling limitations of complicated

systems are such that one might as well view the world as inherently stochastic.

Probability theory is a framework that allows us to deal with uncertainty in

ways that are principled and that bring to bear important and valuable tools.

So first, probabilistic models provide us again this word declarative.

A declarative representation, that is stand alone, where you could look at

a probability distribution and it has clear semantics that represent

our uncertainty about different state that the world might be in.

It also provides us with a toolbox comprising powerful reasoning patterns

that include, for example, conditioning on new forms of evidence or

decision making under uncertainty.

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Finally, the word graphical.

The word graphical is here from the perspective of computer science,

because probabilistic graphical models are a synthesis between ideas from

probability theory in statistics and ideas from computer science.

And the idea here is to use the connections computer science,

specifically that of graphs to allow us to represent systems that are very

complicated that involved large numbers of variables.

And we'd already seen those large number of variable in both of

the applications that we use examples.

Both in the medical example, as well as in the image segmentation example.

And so in order to capture probability distributions over spaces involving

such a large number of factors,

we need to have probability distributions over what are called random variables.

And so the focus of this class and what we'll do for

most of it is to think about the world as represented by a set of random variables,

X1 up to Xn, each of which captures some facet of the world.

So, one symptom that may be present or absent, or

a test result that might have a continuous set of possible values or

a pixel that might have one of several labels.

So each of these is a random variable and

our goal is to capture our uncertainty about the possible states of the world

in terms of their probability distribution or what's called a joint

distribution over the possible assignments to the set of random variables.

Now, the important thing to realize when looking at this,

is that even in the simplest case where each of these is, say, binary valued,

which is not on the case, but say just for sake of the argument.

If you have n binary value variable then this is a distribution,

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Over to to the n possible states of the world.

One for each possible assignment.

And so we have to deal with objects that are intrinsically, exponentially large.

And our only way to do that is by exploiting data structures that encode,

that use ideas from computer science in this case to exploit the structure and

distribution and represent and manipulate it in an effective way.

So what are graphical models?

Let's look at a couple of very simple examples, so

here's a toy Bayesian network,

one that will accompany us through much of the first part of this course.

A Bayesian network is one of the two main classes of probabilistic graphical models,

and it uses a directed graph as the intrinsic representation.

In this case, remember we had a set of random variables X1 up to Xn.

The random variables are represented by nodes in the graph.

So, to take a look at this very simple example which we'll discuss again later,

here we have a situation where we have a student who takes a course and

gets a grade in the course, and so that's one of our random variables.

We have other random variables that are also related to that.

For example, the intelligence of the student's in the course,

the difficulty of the course.

And others that might also be of interest, for

example the quality of the recommendation letter that the student gets in

the course which is dependent on things, perhaps the students' grade, and

these score that the students might receive on the SAT.

So, this is a representation of a probability distribution,

in this case over these five random variables.

And the edges in this graph represent the probabilistic connections between

those random variables in a way that is very formal as we'll define later on.

The other main class of probabilistic graphical model is what's called

the Markov network and that uses an undirected graph.

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And in this case, we have an undirected graph over 4 random variables A, B,

C, D and will give an example of this type of network maybe a little bit later on.

So these were toy examples,

here are some real examples of the same type of framework.

So, this is a real Bayesian network.

It's a network that's actually called CPCS,

it's a real medical diagnosis network.

It was designed at Stanford University for

the purpose of diagnosis of internal diseases and

it has 480 some nodes, and a little bit over 900 edges.

And it was used for diagnosing internal diseases by physicians here.

Another real graphical model, in this case on the Markov network side, is one

that's used for the image segmentation tasks that we talked about before.

Here, the random variables represent

the labels of pixels or superpixels.

So, one per each superpixel say.

And the edges represent,

again probabilistic relationships between the label of a pixel and

the label of an adjacent pixel since these are likely to be related to each other.

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So to summarize, the graphical representation gives us an intuitive and

compact data structure for

capturing these high dimensional probability distributions.

It provides us at the same time, as we'll see later in the course,

a suite of methods for efficient reasoning,

using general purpose algorithms that exploit the graphical structure.

And because of the way in which the graph structure encodes the parameterization of

the probability distribution, we can represent these high-dimensional

probability distribution efficiently using a very small number of parameters.

Which allows us though feasible elicitation by hand from

an expert as well as automatically learning from data.

And in both cases a reduction in the number of parameters is very valuable.

This framework is a very general one and has been applied to a very broad range

of applications and I'm not going to list all of the ones on the slide, and

there is many others that I could have put on the slide had there been more space.

Let me just very briefly mention a few of them on subsequent slides.

So, we've already discussed the image segmentation.

So, just to motivate the benefits of the PGM framework in this context,

let's look at these two images as an example.

Here is the original images,

this is the division of these images into what I mentioned were called superpixels,

which are these sort of slightly larger coherent regions.

And this is what you get if you apply a state of the art machine

learning framework.

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Individual super pixels separately.

So, just trying to classify each superpixel separately and

you can see that you get, especially in the case of the cow,

a total mess with different superpixels having drastically different labels.

You can't even see the cow in this segmented image.

Whereas if you construct the probabilistic graphical model to capture

the global structure of the scene and

the correlations, the probabilistic relationships between these superpixels.

You end up with a much more coherent segmentation that

captures the structure of the scene.

We've already discussed medical diagnosis as an example, so

here's a real world application.

This was something that was fielded on the Microsoft network for

helping parents figure out what was wrong with a sick child and

the site was called OnParenting.

And parents could enter the primary complaint and

then were led through a series of questions that allowed

the system to provide a probability distribution over

the most likely diagnosis with ailing the child.

A very different application is one of textual information extraction.

Where we might have an article from, say, a newspaper and we'd like to take this

unstructured data and convert it into a structured form, where we have some

representation of the people locations, organizations and perhaps relationships.

So one such task might be take this kind of sentence and

recognize that these two words together form a person,

which might not be that hard, given the presence of the word, missus.

But, this is a little bit harder because Green also a word and yet,

we want to identify it as a person.

We might want to then infer that this is a location and

perhaps that this is an organization.

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It turns out that the state of the art methodology for solving this problem is as

a probabilistic classical model where we have a variable for

each word that encodes the label for that word.

For example, here we have the beginning of a person unit and

an intermediate label for the person unit.

And here is another person unit whereas,

here in this variable is we would like to label it as a location.

But we would like to capture, importantly, the correlations between both adjacent

words as well as between non-adjacent words by using this occurrence

of the word Green to infer this occurrence of the word Green is also a name.

A very different example all together is one that implicates

data from multiple sensors.

This occurs in many applications, one such is for

integrating data related to traffic from both sensors that

we might have in the road or on the top of bridges, weather,

information, incident report that we might get in some form.

We'd like to take all this together and

use a model that as it happens was learned from data.

And that model is then used to predict both current and future road speed

including not only on roads that where we have sensors that measure traffic,

but even more interestingly, on roads where traffic wasn't measured.

And it turns out that this was a very successful application

that was fielded in several large cities with very good results.

From a totally different application domain,

probabilistic graphical models have been used very successfully for

discovering new knowledge that we didn't have before.

In this case, the application was biological network reconstruction,

where a biologist measured protein levels of a diverse set of protein

in different conditions under different perturbations.

And from that, they learned the relationship between those proteins and

discovered interactions between those proteins where one was

controlling another, including the ones that were not known before.

So, let's conclude this introduction with an overview of what we'll learn in

the class.

So, we will cover three main pieces related to probabilistic graphical models.

We'll cover representation of PGMs,

including both directed and undirected representation.

We'll also talk about higher level structures that allows to encode more

complicated scenarios.

Including ones that involve temporal structure as well as ones that involve

repeated or relational structure,

specifically a class of model called plate model.

We'll talk about inference or reasoning using these models.

Covering both exact reasoning, where exact probabilities are the outcome,

as well as approximate methods that provide the different trade off regarding

accuracy in computation.

And we'll also talk about how this class of models can be used for

decision making under uncertainty.

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And finally,

we'll talk about how you might learn these models from historical statistical data.

And we'll talk about learning both parameters,

as well as structure of these models automatically.

And dealing with both the somewhat simpler scenario where we have complete

data that is all of the variables are always observed as well as the more

difficult case where some of the variables might not be observed all the time,

or perhaps not at all.

And that introduces an interesting set of complications but

also a wealth of very exciting applications as we'll see.