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

So let's think about the inference.

So we might be computing the correlation coefficient in a sample and then trying to make an inference about the relationship between two variables in a population.

It's still a difficult computation, an NP hard computation, to do inference over Bayes nets in general.

Thus, sampling has an advantage over inference in that we know a procedure for coming up with at least an approximate value for the joint probability distribution, as opposed to exact inference, where the computation may be very complex.

Recognition and figure ground inference needs to co evolve.

Make an effort!

Make an effort.

We have to consider this when we do probabilistic inference.

Make an honest living.

We'll make an exception.

Let's cut the channel and make an island and make an island building.

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.

You make an effort too!

I'll make an appearance soon.

Make up an appropriate prediction.

I make an honest living.

I'll make you an offer.

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.

Don't make an enemy of him.

How do I make an image?

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

You have to break an egg to make an omelet.

You have to break an egg to make an omelet.

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.

You don't have to make an apology.

An illustration may make the point clear.

I make an excellent wife for him.

I must make an apology to her.

I'd like to make an overseas call.

We'll make an exception of your case.

So if I can make an analogy,

I'm an American. I make my destiny.

Or I could make an educated guess.

This was an easy mistake to make.