The Laplace transform of 1 we just did it at beginning of the video was equal to 1 s, if we assume that s is greater than zero.

So what if theta 1 is equal to 0.5. What happens then?

So I'd have to write negative, we're assuming this is going out,

So this spreadsheet, I've plotted the normal distribution.

We're making a graph with n nodes, and again we're going to assume n is some power of 2.

If and only if there's an edge between the vertices i and j in the graph.

But that's not the only thing we have on the left hand side we also have these other three kilograms.

And then we observe the value iteration converged and we're able to define a policy if we're assuming the argmax under the value iteration expression, which I don't spell out over here.

The audio level must not exceed 80 db.

Where'd you be, if he took a drop over and above, and had a fancy to go for you?

Because we'll see later in this video that to some degree, that assumption is one of the biggest drivers of assumption.

let's say they're just really good at their analysis and figuring out market psychology and things like that.

Our profit in the first period is 30,000.

like that one, this is not a particularly good hypothesis.

And I'm assuming that this column right here is 1 32 of this normal area.

There is a door over here, there's a door over here, and a 3rd one way back here.

And I'm assuming that the area of this entire magenta rectangle I already drew, that's 1 or 100 .

So let's for a second, just pick a point on this distribution over here.

If I can reset June's presentation to about 3 or 4 slides ago.

looking at the coin.

And in the previous videos, the constants all merged together.

So this greater than sign will become a less than sign because we're assuming that the x plus 2 is negative.

It could actually be 90 as advertised and you were just unlucky, but we believe that high probability of this is correct or is the data actually contradicting this claim and you should complain that the claim is incorrect.

What do I mean by large portions of data? Let me illustrate this by an example.

In fact, there's a function over here called set noise.

They have all sorts of stuff.

It can be a little messy.

This first section, the select , this is basically what we're selecting.

We are in replay mode, however.

So, let's go back to that normal distribution.

Let us simulate the situation in our IE.

To write it as a improper fraction

Let's work through an example

Whereas before we measured ranges or distances, now we measure bearings or angles.

When you're done with this, you arrive at a number that now combines the cancer hypothesis with the test result.

But it's amazing how much it'll change the model if you make that 3 into a 1 , or if you make it into even a negative 1 or negative 2 .

The important thing here is after the assignment, p and q refer to the same list.

So before I go on with how we're going to approach this problem,

It is an example that is commonly studied in statistics classes.

That's just 1 s.

I'm trying to draw George Washington.

Of course, if you bounce into a wall we stay where we are.

Remember, this is in a situation, if I assume that I don't have a blue forehead.

My first question for you is, to compute the confidence interval, assuming that we can use a confidence interval to detect outliers in the data.

Those are found interatively by the following algorithm.