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rLlZpnT02ZU
TV of P theta and P theta prime is
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the same as the TV between P theta prime and P theta.
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Well, that's pretty obvious from this definition.
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I just flip those two, I get the same number.
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It's actually also true if I take the maximum.
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Those things are completely symmetric in theta and theta
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prime.
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You can just flip them.
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It's non-negative.
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Is that clear to everyone that this thing is non-negative?
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I integrate an absolute value, so this thing
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is going to give me some non-negative number.
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And so if I integrate this non-negative number,
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it's going to be a non-negative number.
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The fact also that it's an area tells me
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that it's going to be non-negative.
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The nice thing is that if TV is equal to zero, then
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the two distributions, the two probabilities are the same.
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That means that for every A, P theta of A
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is equal to P theta prime of A. Now,
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there's two ways to see that.
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The first one is to say that if this integral is
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equal to 0, that means that for almost all X,
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f theta is equal to f theta prime.
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The only way I can integrate a non-negative and get 0
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is that it's 0 pretty much everywhere.
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And so what it means is that the two densities
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have to be the same pretty much everywhere,
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which means that the distributions are the same.
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But this is not really the way you want to do this,
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because you have to understand what
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pretty much everywhere means--
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which I should really say almost everywhere.
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That's the formal way of saying it.
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But let's go to this definition--
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which is gone.
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Yeah.
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That's the one here.
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The max of those two guys, if this maximum is equal to 0--
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I have a maximum of non-negative numbers, their absolute values.
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Their maximum is equal to 0, well,
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they better be all equal to 0, because if one is not
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equal to 0, then the maximum is not equal to 0.
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So those two guys, for those two things
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to be-- for the maximum to be equal to 0,
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then each of the individual absolute values
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have to be equal to 0, which means that the probability here
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is equal to this probability here for every event A.
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So those two things--
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this is nice, right?
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That's called definiteness.
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The total variation equal to 0 implies that P theta
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is equal to P theta prime.
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So that's really some notion of distance, right?
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That's what we want.
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If this thing being small implied
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that P theta could be all over the place compared
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to P theta prime, that would not help very much.
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Now, there's also the triangle inequality
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that follows immediately from the triangle
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inequality inside this guy.
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If I squeeze in some f theta prime prime in there,
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I'm going to use the triangle inequality
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and get the triangle inequality for the whole thing.
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Yeah?
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AUDIENCE: The fact that you need two definitions
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of the [INAUDIBLE],, is it something
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obvious or is it complete?
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PHILIPPE RIGOLLET: I'll do it for you now.
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So let's just prove that those two things are actually
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giving me the same definition.
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So what I'm going to do is I'm actually going
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to start with the second one.
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And I'm going to write--
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I'm going to start with the density version.
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But as an exercise, you can do it for the PMF version
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if you prefer.
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So I'm going to start with the fact that f--
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so I'm going to write f of g so I don't have to write f and g.
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So think of this as being f sub theta, and think of this guy
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as being f sub theta prime.
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I just don't want to have to write indices all the time.
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So I'm going to start with this thing, the integral of f
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of X minus g of X dx.
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The first thing I'm going to do is this is an absolute value,
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so either the number in the absolute value is positive
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and I actually kept it like that, or it's negative
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and I flipped its sign.
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So let's just split between those two cases.
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So this thing is equal to 1/2 the integral of--
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so let me actually write the set A star as
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being the set of X's such that f of X is larger than g of X.
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So that's the set on which the difference is
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going to be positive or the difference is
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going to be negative.
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So this, again, is equivalent to f
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of X minus g of X is positive.
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OK.
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Everybody agrees?
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So this is the set I'm interested in.