Poisson Error Bars Histogram
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Poisson Uncertainty
ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is histogram error bars a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a histogram error bars matlab minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Histogram PDF Fitting - bin Variance Poisson or not up vote 1 down vote favorite So http://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735 my basic query is just because you've binned and counted the data into a histogram are the error bars on the bins necessarily the Poisson $\sqrt{N}$? Longer explanation: I have an image of pixel values which represent brightness values. I am binning them into a histogram to get the PDF of the brightness distribution and then trying to fit a predicted model PDF to that. I'm fitting just using chi-square/least squares but if I use $\sqrt{N}$ as the error bars then there is http://stats.stackexchange.com/questions/68487/histogram-pdf-fitting-bin-variance-poisson-or-not basically never a "good" fit (reduced chi-squared of 3 or 4 for ~200 DOF) even for models we think should be good fits, the error bars seem too small. We only have one set of real data so I tried using one model and generating a bunch (~30,000) of simulated images drawing the pixel values from the model and randomizing the positions and making histograms for each one. I then compute the mean of each bin from these simulations and the standard deviation. The standard deviations are definitely larger than $\sqrt{N}$. And now if I take the PDF from one of those and compare it the predicted model PDF it gives a good chi squared using the errors from the simulation but again still bad if I use Poisson variance. But my advisor/boss doesn't agree/understand. He says we're just counting so there is no reason that the error bars should be larger. My idea is that they're larger/not Poisson because its not a totally random process. The number of pixels in a bin is dependent upon the underlying model and how well your particular realization/image samples that underlying distribution. And we are not using the likelihood because its difficult to interpret for goodness of fit. poisson histogram fitting share|improve this question edited Aug 27 '13 at 19:42 Comp_Warrior 1,277926 asked Aug 27 '13 at 19:34 cinwick2321 112 If the model is right and the observations are independent, it should be Poisson (In fact, if yo
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