goodness-of-fit | 易学教程

How to perform a chi-squared goodness of fit test using scientific libraries in Python?

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问题 Let's assume I have some data I obtained empirically: from scipy import stats size = 10000 x = 10 * stats.expon.rvs(size=size) + 0.2 * np.random.uniform(size=size) It is exponentially distributed (with some noise) and I want to verify this using a chi-squared goodness of fit (GoF) test. What is the simplest way of doing this using the standard scientific libraries in Python (e.g. scipy or statsmodels) with the least amount of manual steps and assumptions? I can fit a model with: param = stats

goodness of fit in pymc and plotting discrepancies

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问题 I'm using PYMC 2.3.4. I found terrific. Now I would like to do some goodness of the fit and plot discrpancies how shown in section 7.3 of the documentation (https://pymc-devs.github.io/pymc/modelchecking.html) In the documentation they say that you need 3 inputs for the discrepancy plot x: the data x_sim: the posterior distribution sample x_exp:expected value I can understand the first two but not the third This the code Sero=[0,1,4,2,2,7,13,17,90] Pop=[ 15,145,170,132,107,57,68,57,251] for i

goodness of fit in pymc and plotting discrepancies

阅读更多关于 goodness of fit in pymc and plotting discrepancies

I'm using PYMC 2.3.4. I found terrific. Now I would like to do some goodness of the fit and plot discrpancies how shown in section 7.3 of the documentation ( https://pymc-devs.github.io/pymc/modelchecking.html ) In the documentation they say that you need 3 inputs for the discrepancy plot x: the data x_sim: the posterior distribution sample x_exp:expected value I can understand the first two but not the third This the code Sero=[0,1,4,2,2,7,13,17,90] Pop=[ 15,145,170,132,107,57,68,57,251] for i in range(len(Pop)): prob[i] = pymc.Uniform(`prob_%i' % i, 0,1.0) serobservation=pymc.Binomial(

Keypoint Descriptor Matching: How to calculate a goodness-of-fit per template?

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问题 I am not sure whether this belongs on stackoverflow or another stackexchange site - input very welcome here. I have used python OpenCV to match a target image's BRISK keypoint descriptors to - in turn - three different templates. What is a practical, robust, statistically-sound way to decide which template is the best-fitting one? Right now I calculate the number of cv2.RANSAC inliers returned by cv2.findHomography (which incidentally doesn't return a goodness-of-fit statistic) and take the