Probabilistic Interference

It's probabilistic and it's messy.

But that's a probabilistic statement.

Now, we've seen probabilistic word models before.

Let's build a probabilistic word model of segmentation.

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

Data, data, data...

Expectation maximization is a probabilistic generalization of k means.

Bing! That's data. Data, data, data.

We have to consider this when we do probabilistic inference.

Hello and welcome to the class in Probabilistic Graphical Models.

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

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

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

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

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.

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

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

Say sorted data is data.

little data, old data, no data. So back in 1995,

Data

Data...

Data

Data

Data

Data

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

like DNA data or brain data.

Raw data now! Raw data now!

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.

looking at wind data and solar data.

Data Viewer

(binary data)

raw data

? target data?

Merge data

Data Mode

Data mode

Data size