Author Topic: The "Suspension of Fuckwittery" Challenge Thread - Q&A Sessions - 2007  (Read 3936 times)

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Offline Calandale

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Where is the Core of Life so I can assimilate it, therefore becoming God incarnate?

* purposefulinsanity quakes in fear.   :o

Maybe the Buddhists have it. They always act so damn neutral as if they're hiding summat.

If Buddhists in England start getting cannibalized,
we'll know who's at fault.

The_P

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Where is the Core of Life so I can assimilate it, therefore becoming God incarnate?

* purposefulinsanity quakes in fear.   :o

Maybe the Buddhists have it. They always act so damn neutral as if they're hiding summat.

If Buddhists in England start getting cannibalized,
we'll know who's at fault.

Better than to be some generic worker at Tescos.

purposefulinsanity

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Offline Nomaken

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Can someone explain to me why we can't find both the position and the velocity of a subatomic particle?  If our observation of one changes the other, couldn't we figure out how our observation changes the other, and by how much, and then just correct our measurement?
And as always, these are simply my worthless opinions.
Reverence is fine, Sanctity is silly.
We're all fucked, it helps to remember that.

Offline maldoror

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Can someone explain to me why we can't find both the position and the velocity of a subatomic particle?  If our observation of one changes the other, couldn't we figure out how our observation changes the other, and by how much, and then just correct our measurement?

And how would we do that?
!!Super atomic enema!!

Offline Christopher McCandless

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Can someone explain to me why we can't find both the position and the velocity of a subatomic particle?  If our observation of one changes the other, couldn't we figure out how our observation changes the other, and by how much, and then just correct our measurement?
Wave Particle Duality, I think basically the particle is both a wave and a particle if that makes sense? Also to measure velocity you would have to take two positions of a particle to do so, which if you cant take a single position you cant find the velocity.

Kosmonaut

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?

Offline Christopher McCandless

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter

Kosmonaut

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.

Offline Christopher McCandless

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.
Ok, then the second premise where not all the information is not there, or not to a level of accuracy. A simple example is the Radar, there is always interference with the signal. The filter approximates to find and enemy location, in a similar fashion to the Bayesian stats used in GPS.

Offline McGiver

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.
Ok, then the second premise where not all the information is not there, or not to a level of accuracy. A simple example is the Radar, there is always interference with the signal. The filter approximates to find and enemy location, in a similar fashion to the Bayesian stats used in GPS.
i bet it is a very long time before you get laid.
Misunderstood.

Offline Christopher McCandless

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.
Ok, then the second premise where not all the information is not there, or not to a level of accuracy. A simple example is the Radar, there is always interference with the signal. The filter approximates to find and enemy location, in a similar fashion to the Bayesian stats used in GPS.
i bet it is a very long time before you get laid.
I doubt it McJ, I can get laid when I want to, I have pretty high levels of personal charisma. Bear in mind maths is my specialism really, so I do know the stuff...

Offline McGiver

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fair enough.
so, have you ever been laid?
Misunderstood.

Kosmonaut

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.
Ok, then the second premise where not all the information is not there, or not to a level of accuracy. A simple example is the Radar, there is always interference with the signal. The filter approximates to find and enemy location, in a similar fashion to the Bayesian stats used in GPS.
Well, i have no idea what you mean by similar fashion to Bayesian stats used in GPS.
The Kalman filter is as highly dependant on the initial estimate as of the state variables.
Given poor level of initial conditions or information, it does not matter how efficient your filter is you are not going to get any convergence. ie. decent predicition, without the initial predictor being within a certain boundary.
If this is the case, then the Kalman filter is only really measuring parameters rather than predicting, and once these parameters are estimated within what you called a level of accuracy, then we are back to how good your initial prediction is, in deciding how effective the filter is.
This is my initial question.
ie. Why bother with the filter, and just concentrate on the estimator ?


Offline Christopher McCandless

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What is the point in a Kalman filter ? Why don't people just come up with a decent estimator in the first place and cut out all the bullshit ?
Lack of computational power or lack of information. In both cases you can only approximate. Look at this. http://en.wikipedia.org/wiki/Kalman_filter
I am aware of wikipedia.
I refer you to the original question. You may assume infinite computational power.
Ok, then the second premise where not all the information is not there, or not to a level of accuracy. A simple example is the Radar, there is always interference with the signal. The filter approximates to find and enemy location, in a similar fashion to the Bayesian stats used in GPS.
Well, i have no idea what you mean by similar fashion to Bayesian stats used in GPS.
The Kalman filter is as highly dependant on the initial estimate as of the state variables.
Given poor level of initial conditions or information, it does not matter how efficient your filter is you are not going to get any convergence. ie. decent predicition, without the initial predictor being within a certain boundary.
If this is the case, then the Kalman filter is only really measuring parameters rather than predicting, and once these parameters are estimated within what you called a level of accuracy, then we are back to how good your initial prediction is, in deciding how effective the filter is.
This is my initial question.
ie. Why bother with the filter, and just concentrate on the estimator ?


I assume the filter helps the estimator stage, maybe the algorithms cannot be applied directly.