We have to consider this when we do probabilistic inference.

In this unit, we will see how to do probabilistic inference.

We're going to talk about perhaps the holy grail of probabilistic inference.

It's the most fundamental consideration in probabilistic inference, but the basic rule is really, really simple.

Rather than call them input and output variables, in probabilistic inference, we'll call them evidence and query variables.

Probabilistic Interference

The fact is that the thing we programmed here captures the key of the probabilistic inference necessary to localize the Google care.

And now furthermore, in probabilistic inference, the output is not a single number for each of the query variables, but rather, it's a probability distribution.

It's probabilistic and it's messy.

But that's a probabilistic statement.

So let's think about the inference.

Now, we've seen probabilistic word models before.

And the key idea to Bayesian inference is you have two sources of information from which to make your inference.

Let's build a probabilistic word model of segmentation.

That is, I've said for my probabilistic model,

Recognition and figure ground inference needs to co evolve.

Expectation maximization is a probabilistic generalization of k means.

Hello and welcome to the class in Probabilistic Graphical Models.

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

We have a very adaptive sort of online type inference algorithm.

Later on, Peter will tell you about inference in Bayes networks.

And what it means is we really are Bayesian inference machines.

So that was the word model . What about probabilistic ? The word

You could have trees and have a probabilistic model of them.

And training these probabilistic models in general can be very hard.

And that additional assumption would have to be probabilistic in nature.

Or the truthiness is out there. Since this is all probabilistic.

The next technique for efficient inference is to maximize independence of variables.

Now, what kinds of questions can we ask to do inference about?

Now I want to talk about approximate inference by means of sampling.

And you can't do a reverse inference here. You can't say that.

Variable elimination works faster than inference by enumeration in most practical cases.

It's also sharing some electrons in a covalent bond and everything's probabilistic.

In our JavaScript Engine, we've recently had a great boost from type inference.

It's a Boolean logical distinction rather than on this side a probabilistic distinction.

We've seen how to do enumeration to solve the inference problem on belief networks.

We discussed specific incidents of hidden Markov model inference or filtering in our quizzes.

Before drawing the line, hover over the top edge and find the midpoint inference.

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

So Probabilistic Graphical Models are a framework for dealing with this kind of application.

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

Now let's do one more example of a probabilistic problem this time, spelling correction.

Here's a quiz to make sure you understand how to calculate these probabilistic models.

A kind of inference where we are trying to say, what's the most likely explanation?

The nice thing about this structure is it makes it possible to efficiently do inference.