# Difference between revisions of "Thread:User talk:Tmservo/does bin smoothing make guns better or worse/reply (4)"

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− | @MN and Skilgannon: While those things are true, I think those descriptions | + | @MN and Skilgannon: While those things are true, I think those descriptions miss the underlying point of why bin smoothing is helpful. In both targeting and dodging, what one is trying to do is to estimate the PDF ([[wikipedia:Probability density function|probability density function]]) curve of the opponent's movement/targeting. Whenever one is estimating a PDF curve (not just in Robocode) from a finite number of observations, and the system has some inherent noise or unpredictability, it will give a more accurate estimation of the true PDF curve if one applies the correct amount of smoothing. Now, how much is the "correct amount" is a complicated question that depends on how much noise/uncertainty your measurements have, how much noise/uncertainty the process being measured has, and how many measurements you have (reminds me of Kalman filters, similar principals come into play). In Robocode most people just pick a certain amount of bin smoothing that seems to work for them, but I feel it is worth pointing out that this is just one application of PDF curve estimation, and that there are methods of estimating the correct amount of smoothing which to my knowledge have not yet been applied to Robocode. |

+ | (I'd guess you two already are well aware of this, but I just feel like rambling) |

## Latest revision as of 20:54, 23 November 2013

@MN and Skilgannon: While those things are true, I think those descriptions miss the underlying point of why bin smoothing is helpful. In both targeting and dodging, what one is trying to do is to estimate the PDF (probability density function) curve of the opponent's movement/targeting. Whenever one is estimating a PDF curve (not just in Robocode) from a finite number of observations, and the system has some inherent noise or unpredictability, it will give a more accurate estimation of the true PDF curve if one applies the correct amount of smoothing. Now, how much is the "correct amount" is a complicated question that depends on how much noise/uncertainty your measurements have, how much noise/uncertainty the process being measured has, and how many measurements you have (reminds me of Kalman filters, similar principals come into play). In Robocode most people just pick a certain amount of bin smoothing that seems to work for them, but I feel it is worth pointing out that this is just one application of PDF curve estimation, and that there are methods of estimating the correct amount of smoothing which to my knowledge have not yet been applied to Robocode. (I'd guess you two already are well aware of this, but I just feel like rambling)