Narrator So, I'm going to ask you a tricky question and maybe you can calculate this.

Please check yes or no.

We end up walking around in this space of assignments of variables randomly.

We start as before.

There's always a little fluctuation from sample to sample.

The standard deviation is much lower.

Now we just repeat. We look at the next measurement. We weight particles accordingly.

It ignores measurements, because the measurement sets the weighting factor in this resampling process, but with only 1 particle the same particle will be resampled no matter what.

Is it possible that P1 is never sampled in the resampling step?

First in the 70s with groups like Aerosmith, Heart and Boston, then during the eighties heavy metal craze, and on into the era of sampling.

And the way you derive this is by asking the question, what's the probability that particle 'A' is never sampled?

Confidence

Adaptive

Always sample

Maximum sample

Sample radius

Then you've got a really big range.

And assuming we got a probability of 0.9 came out in favor of the w, that would be the end of the sample.

Let's say we sample that randomly, and it comes up Rain is positive.

This verdict essentially rendered any kind of sampling, no matter how small, infringing.

And this procedure would also be consistent.

Put differently, this particle x1 that sits on a lane marker is being regenerated in the resampling phase with a probability of 0.2963.

I must say i wasn't working there at that time. It was great, lot of fun,

And how do we get the standard error, and what is standard error?

But the magnitude of correlation also depends upon sampling.

This robot moves and it moves by using its previous particles to create a new random particle set that represents the posterior probability of being at a new location.

So the DJ's that want it to keep on playing this music would program drum machines, and add little pieces of disco records that they would loop

Its all about Drum machines

Well, just looking at that little example with the wine experts.

In this case, let's choose Sprinkler, and we look at the rows in the table for which Cloudy, the parent, is positive, and we see we should sample with probability 10 to s and 90 a s.

That was a really cool challenge that one. We stayed up all night on the beat, 24 hours.

We now sample 3 new particles, with replacement.

Then we could throw all this information out and start over again by having another 50 50 choice for cloudy and then working our way through the network.

And those 2 variables in turn have an effect on whether the grass gets wet.

0.6, 1.2, 2.4, 0.6, and 1.2.

So I read in the complete works of Shakespeare into a small computer program, and then built n gram models and sampled from that model.

Im not used to working like that. Im used to working my own times my own means

And the answer is 0.0777 approximately, and the way to obtain this is for this particle to never to be drawn in the resampling phase.

So again, think about the wine experts and the wine ratings.

We're in Lecture five, the second segment and the last segment of Lecture five and the last segment before we're ready to jump in to inferential statistics.

For ignoring robot motion that's not the case.

DVR Support Kris Smith

So, this is essentially my implementation for the resampling step.

We get to keep every sample, but we have a problem.

But their importance weights add up to 0.8.