And it makes much more sense to talk about the probability or random variable equaling a value, or the probability that it is less than or greater than something or the probability that is has some property

But even at a higher level, the modeling limitations of complicated systems are such that, one might as well view the world as inherently stochastic.

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

Yes or no.

And here's my code, this implements Bayes rule.

And what we know about its distribution, its, its properties, what percentage of scores fall in certain regions.

It might be effective.

And the answer is 0.45.

For example, that's how we can make the quad hover.

And then we told them the average likelihood of someone like them to suffer these misfortunes.

So, this probability is equal to the product over all i of the probability of words of i given all the subsequent words. So that would be from word 1 up to word i 1.

First, it can only handle true and false values.

And what's the same for P( C, Neg).

I want you to think about how you design computer programs to solve particular problems.

And using the prior, we know that P of not C is 0.99.

In this class so far we talked a good deal about planning.

The tools that we develop in this course are going to be useful across the board here, but we're going to mainly focus on issues of time.

It is continuous that is all measurements are continuous and all actions tend to be continuous.

And these are mutually exclusive events, you can't have both of them

In a deterministic action, it obviously succeeds, unless of course we run into a wall.

Or is it observer relative?

That's the probability of given that we do have cancer.

The observation partitions the belief state into smaller belief states.

To make this correct, we also have to compute the posterior for the non cancer option, which there is no cancer given a positive test.

So this little thing over here is my shortcut of not having cancer.

Now comes the hard part

And so you're going to have uncertainty and variability that is simply due to modeling limitations.

Then the value of the state for the action go up would be obtained as follows.

K means is a nice, intuitive algorithm to derive clusterings.

It's continuous in its state and action spaces and its measurement, yes or no?

Once I tried to calculate them, but they're astronomical.

Probability

Phil, the odds against

Here you get 0.72, which is the product of not having cancer in the first place 0.9 and the probability of getting a negative test result under the condition of not having cancer.

In general we don't try to please the customer here.

This is a book on Optimal Transport, it is a problem involving mathematics, engineering, probability and geometry everything that was said.

When you receive, say, a positive test result, what you do is, you take your prior P(C) you multiply in the probability of this test result, given C, and you multiply in the probability of the test result given (Neg_BAR_ C).

So 0.5 times 0.95 gives you 0.45 whereas the unfair coin, the probability of tails is 0.1 multiply by the probability of picking it at 0.5 gives us 0.05

let's first figure out what they might be used for.

Please say yes or no.

So this bond becomes a Double A bond.

So what type of game is this?

You might say OK, that's the probably of getting exactly 1 times the probability of getting 2 out of 3 plus the probability of getting 3 out of 3.

I want to spend a few minutes and go over the formal definition of localization.

What are the odds?