Derive Yourself a Kalman Filter

Re:Derive Yourself a Kalman Filter by bonoboTP on Monday May 13, 2019 @09:54PM (#19906102)

It's getting a little tedious. Please show me a concrete citation of a serious textbook (not a tutorial/handout by a grad student or a paper by a random researcher) that puts the three words "conditional random variable" next to each other (consistently, not simply as a one-off potential mistake). Google doesn't show serious sources for it.

While I agree with isolated points of your comment I think it doesn't add up to a useful/coherent concept of conditional random variable.

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Re:Derive Yourself a Kalman Filter by srean on Tuesday May 14, 2019 @12:17PM (#19910878)

Thats a little too much to ask, perhaps if they were grep'able I could have obliged, unfortunately I dont have a photographic memory.

More concretely its just another name for conditional expectation. I am assuming you are aware that conditional expectation is a random variable obtained via conditioning (equivalently as a piecewise approximation in L_2). If you arent familiar with that view point that would be the place to start. Kolmogorov, Neveu, Dudely, Billingsley will all cover that view point.

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Re:Derive Yourself a Kalman Filter by bonoboTP on Wednesday May 15, 2019 @12:45PM (#19921217)

> I am assuming you are aware that conditional expectation is a random variable

That's not what we're considering here, but things of the form X|Y=y for a concrete y. Even as E[X|Y=y], that's not a function, y is specified. Do you agree we shouldn't call X|Y=y a conditional random variable?

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Re:Derive Yourself a Kalman Filter by srean on Wednesday May 15, 2019 @02:50PM (#19922497)

Oh absolutely for a specific y its not function (or a random variable) one usually thinks of Y as a variable and not a constant.

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Re:Derive Yourself a Kalman Filter by kgwgk on Wednesday May 15, 2019 @04:22PM (#19923346)

The expectation E[X|Y=y] is a fixed value. (Edit: it’s the expectation of the random variable “X|Y=y”, while E[X|Y] is a random variable because it’s a function of the random variable Y: for each element in the sample space there is a corresponding value of “y” and in turn there is a value of the expectation E[X|Y=y].)

X|Y=y (as used in the blog post being discussed) is a random variable: it’s a function from a subset of the original sample space (corresponding to the elements for which the value of the random variable Y is y) to real values (or whatever the image of the X random variable is).

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Re:Derive Yourself a Kalman Filter by srean on Thursday May 16, 2019 @01:53PM (#19931183)

Yes you are right. I had messed up in the comment above. It continues to be a function on the restriction Y=y

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