So let's think about the inference.

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

Recognition and figure ground inference needs to co evolve.

We have to consider this when we do probabilistic inference.

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.

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.

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

Human pride is human weakness.

It's chatting human to human and finding that human connection.

human?

Human

Human?

Human?

Human.

Human.

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.

A human player beats another human player

More human than human is our motto.

When I'm human again Only human again

When we're human again Only human again

We'll be human again Only human again

You are human, and yet not human.

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.

Human wins

Human loses

Human nature.

Human trafficking.

Human rights?

Human rights.

Human feces?

Human again.

She's human!

Only human.

A human?