He wins his arguments by logical reasoning.

This one has no y at all in it.

it's a theory.

I'm asking if it's possible.

This meant they must have been closer together in the past.

Think of all the roles that people play in arguments.

If you won't see my line of reasoning, it's war between us.

Eventually, I was able to deduce most of their huge logical alphabet.

For every one of every two of these horses, there's one dog.

They are interested in abstract reasoning.

We're born problem solvers.

I have a theory, but the, uh...

I call it an inevitable conclusion based on reprehensible process of reasoning.

I will now teach you about estimators.

That is deductive reasoning.

Well, I do think there's a good framework for thinking.

This stuff is also really easy to relearn, provided you have a really strong grounding in reasoning.

But thinking is really very straightforward it's just sort of some kind of logical reasoning, or something.

Do you guys see how this, right here, compared to that which one creates that patient problem solving, that math reasoning?

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

Jeremy Bentham, the eighteenth century English political philosopher.

But if you see a couple of examples and you see a pattern there you can often extrapolate and get to a kind of broader generalization.

Mu and sigma squared.

So, tools for dynamic inference of invariants can work well if they do have a good test suite in the beginning.

And numbers in between give you the gray levels of uncertainty.

That's just not practical, so we're going to have to come up with methods that are faster than enumerating everything.

There's the audience in rhetorical arguments.

Here's my argument. Does it work? Is it any good?

Y is either zero or one, but if you are using

What data and observations did you filter in as a result of your beliefs, and why?

We don't care about the test result as much as we care about the fact whether we have cancer or not.

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

It's actually a big name for little thing but this little thing again has big consequences.

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

On our fourth rung, a very crucial thing happens.

Generally I think there are what I mean by that is, boil things down to their fundamental truths and reason up from there, as opposed to reasoning by analogy.

Find you... between midnight and sunrise tomorrow, freedom for both of you.

And interestingly, wearing a lap only seatbelt, 16.7 percent die.

You might have guess it correctly. It's the uniform distribution.

So this unit is a tough one.

You're wide of the mark.

Your guess is wrong.

A second terminology for environments pertains to whether the environment is deterministic or stochastic.

'The world' is objective,

How dare you presume to...