You have descriptive.

Enter descriptive label here

Descriptive name for the device

Make it short and descriptive.

That would be descriptive statistics.

So let's think about the inference.

A descriptive name for the profile

Well, we could just run descriptive statistics.

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

A book descriptive of the wonders of nature.

So, here's some descriptive statistics for this example.

And we'll plot histograms and get descriptive statistics.

Recognition and figure ground inference needs to co evolve.

Insert a short descriptive name of your project here.

Enter any descriptive name you like for this filter.

So there are the descriptive statistics, or summary statistics.

We'll see when we look at the descriptive statistics.

In the next video, we'll explore more descriptive statistics.

Just like we saw way back in descriptive statics.

We have to consider this when we do probabilistic inference.

Think back to descriptive statistics and the lecture on correlations.

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

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

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

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

Because I don't think Student Loans is a very descriptive name.

Descriptive Statistics. As it's said in the first lecture, most introductory

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

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

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

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.

The following descriptive analysis was derived from over 1500 closely monitored questionnaires.

Welcome back. We're on segment three of Lecture 2 on descriptive statistics.

Hi. Welcome to the second segment, of lecture two, on descriptive statistics.

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

I can get the descriptive statistics and then I can just run correlations.

You know that's useful when you want to have more descriptive text here.

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.

And remember we did this by hand when we did descriptive statics and correlations.

So then what it does is it gives me two sets of descriptive statistics.

Let's just get into the meat of it and we'll start with the descriptive.