With likelihood weighting, we fix the evidence variables.

And this weighting is relatively straightforward to apply in Gaussian fitting.

So, if we measured zero point zero zero seven zero zero kilometers.

So what's zero, plus zero, plus zero, plus zero?

One, zero, zero.

Zero plus zero plus zero ten times is still zero.

1, zero, minus 1, zero, 1, zero.

So 1, zero, minus 1, zero, positive, zero, negative, zero, positive.

One, one, point zero zero one, zero zero one, dot dot dot.

Sine of zero is zero, so minus zero is still zero, so this is zero. f prime prime of zero.

Likelihood weighting is a great technique, but it doesn't solve all our problems.

How many 5s have I seen? Zero, zero, zero, zero, 1, 2, great.

So, times zero, times zero.

Anything times zero is zero.

Zero times 3 is zero.

Sine of zero is zero.

Zero

Zero

Zero.

Zero.

Zero.

Zero.

Zero.

So four plus zero plus zero.

And you get six zero zero.

Zero times twenty five is zero.

So, anything times zero is zero!

Eight point two five minus zero point zero one zero five.

So you don't have to memorize your zero multiplication tables because everything times zero is zero, or zero times anything is zero.

You know, let's say, usually you initialize your parameters at zero for zero, theta zero and zero.

And because of this left weighting, the average is actually here, way to the left.

Sign of zero is zero, two times zero is zero, so this whole thing is zero. 1 minus 0 is 1. Fair enough.

So zero to any non zero number, you are going to get zero.

So it's eight point two five and zero point zero one zero five.

Zero Cornered

Zero values

Six zero.

Seven zero.

It's zero.

Well zero.

Zero Six?

Zero. Zip.

And twenty five goes into zero zero times.

The absolute value of zero is just zero.

For zero to the zeroth being zero and zero to the zeroth power being one.