statistics

问题 I am having a small issue but I am clueless where I am at fault. Can someone please guide me the right way? Thanks in advance. What I have done. My codes finds local maxima’s. Bring down from local maxima to a certain point. Assign the neighbors that are greater than the downsized value, the value of downsized point. Small Example X = [1 0 1 4.3 4.5 5 4.3 4.2 0 0 0 2 6.2 6.3 7 6.2 7.4 8 7.2 1 2 3 4 2]; Local maxima’s are 5, 7, 8, and 4 Go down to certain point. Like 4, 6, 7, 3. Assign

问题 Is there any way to increase the decimal accuracy for probability distributions. > 1-pchisq(90, 5) [1] 0 But: > pchisq(90, 5, lower.tail=F) [1] 6.71932e-18 回答1: If you are doing the 1 - foo to get the upper tail of the distribution, then don't. Instead use the lower.tail argument: > pchisq(90, 5, lower.tail = FALSE) [1] 6.719319e-18 来源： https://stackoverflow.com/questions/6704365/pchisq-increase-decimal-accurancy

问题 I need to compute the mutual information, and so the shannon entropy of N variables. I wrote a code that compute shannon entropy of certain distribution. Let's say that I have a variable x, array of numbers. Following the definition of shannon entropy I need to compute the probability density function normalized, so using the numpy.histogram is easy to get it. import scipy.integrate as scint from numpy import* from scipy import* def shannon_entropy(a, bins): p,binedg= histogram(a,bins,normed

问题 I want to generate a file containing timestamps (integers between 0 and a bound value x, in increasing order) which represents arrivals of an event. The "Event arrival rate" should be "normal distributed" which means, somehow in the "middle" of the dataset the rate of arrivals should be more frequently as at the beginning and the end. How can i generate such a list of values using java? regards 回答1: I agree with greedybuddha that a Gaussian function is what you want here, but you also stated

问题 I am trying to make good use of the D3 histogram function and am struggling due to an awkward fat tail data distribution. The data_points array below pertain to country population densities across multiple years. Cities like Hong Kong with high population densities are responsible for the fat tail. I would like to split the data into eight separate buckets. I have not specified a domain, by which I assume the histogram function will use the min and the max. My code is as follows: var bins =

问题 So one of the standard things to do with the data is normalize it and standardize it to have data that's normally distributed with a mean 0 and standard deviation of 1, right? But, what if the data is NOT normally distributed? Also, does the desired output has to be normally distributed too? What if I want my feedforward net to classify between two classes (-1, and 1), that would be impossible to standardize into a normal distribution of mean 0 and std of 1 right? Feedforward nets are non

问题 I have .las (lidar data) file, now I wanted to know the size of it, for example how long it's width and height in kilometers. How is it possible to retrieve these kind of info? 回答1: tl;dr: install libLAS and run lasinfo myfile.las . Lasfile headers, in general, shouldn't be trusted since they may not agree with the true data bounds. A more robust approach is to calculate the spatial extents of the data by reading the points themselves. Here's a few free and open-source ways to calculate the

问题 I have 2 columns in MS SQL one is Serial no. and other is values. I need the thrird column which gives me the sum of the value in that row and the next 2. Ex SNo values 1 2 2 3 3 1 4 2 5 6 7 9 8 3 9 2 So I need third column which has sum of 2+3+1, 3+1+2 and So on, so the 8th and 9th row will not have any values: 1 2 6 2 3 6 3 1 4 4 2 5 5 1 6 7 2 7 8 3 9 2 Can the Solution be generic so that I can Varry the current window size of adding 3 numbers to a bigger number say 60. 回答1: Here is the SQL

问题 I have some satellite data which looks like the following (scatter plot): I now want to bin this data into a regular grid over time and latitude and have each bin be equal to the mean of the all the data points that fall within it. I have been experimenting with scipy.stats.binned_statistic_2d and am baffled at the results I am getting. First, if I pass the "count" statistic into the scipy binning function, it appears to work correctly (minimal code and plot below). id1 = np.ma.masked_where

问题 I've started doing more and more work with the new Mathematica statistics and data analysis features. I attended the "Statistics & Data Analysis with Mathematica" online seminar on Tuesday (great presentation, I highly recommend it) but I've run into some problems that I hope someone on this forum might have a few moments to consider. I've created a rather extensive notebook to streamline my data analysis, call it "AnalysisNotebook". It outputs an extensive series of charts and data including