In this question, we assume that a particle,

Let's call big W the sum of all these weights, and let's normalize them just for the consideration of what to do, and it's called the normalized weights alpha.

Use Normalized Name

Normalized

Normalize

And then you just normalize all of those, so they add up to one.

Normalized Subject

Then I compute the normalizer, which I'LL call α, is the sum of all these guys over here.

I should mention one important point with normal interpolation.

Narrator And the answer is 0.1, 0.2, 0.4, 0.1, and 0.2, and to see we just have to normalize those importance weights.

That isn't perfect.

We normalize, so the average brightness is zero.

We multiply those together.

How did we get there?

We do the same thing with not A.

And the answer is measurements are indeed ignored which has to do with the following

And I'm going to ask you the same question about the normalized importance weight of particle 'A', if the measurement is 'black'.

We already talked about this example earlier in class, but now it's in the content of normal distribution so let me take a second to show this to you.

Our total weight for all particles, not normalized particles, will be 2.7 or 27 tenths.

We also compute an importance weight, which is the measurement probability for that specific particle over here.

The total weight over here is 2.4, and the total weight over here is 0.3.

And if you plug in our Gaussian formula, you get the following

All right.

So, for small values of

Now I'd like to what's the normalized importance weight, after normalization, of the particle, labeled 'A', if our measurement happens to be 'white'?

And that's obviously 0.0999.

And then we can recover the original probabilities by normalizing based on those values over here, so the probability of A given B, the actual probability, is a normalizer, eta, times this non normalized form over here.

I'd like to do in this video, is get a better understanding of how cross validation and training error vary as we as we vary the regularization parameter lambda.

Then the new mean, mu prime, is the weighted sum of the old means where mu is weighted by r squared and nu is weighted by sigma squared normalized by the sum of the weight factors.

0.1 and 0.1 again.

Regular expression that is to be replaced.

This is the quintessential term that occurs in the variance calculation of x.

It's about the same values but, according to this calculation over here, they'd be different.

C plays a role similar to one over Lambda, where Lambda is the regularization parameter we had previously.

You programmed a product between the prior probability distribution and a number.

The net weight of this jam is 200 grams.

So here's the 100,000 question.

For now we just add them into our new particle set as prime and we reiterate.

That's the same join for green is 1 times 0.2.

But this one is just like the variance calculation but it mixes x's and y's whereas these are x² and y².

So, take a look at this for a moment.

The same for the opposite event of no fire given the neighbor just said, yes, it burns.

But whatever this third expression was, that's what I'd want this subtree this subpart of my tuple to be.

This is clearly not a probability because we left out the normalizer.

Now let me show you how we do that.