In general, you'll only want to normalize your data if you're going to be using a machine learning or statistics technique that assumes your data is normally distributed. Some examples of these includet-tests, ANOVAs, linear regression, linear discriminantanalysis(LDA) and Gaussian naive ...
As adjectives the difference between usual and normal is that usual is most commonly occurring while normal is...
Python numpy.random.normal() MethodThe numpy.random.normal() draw random samples from a normal (Gaussian) distribution. It takes some parameters like loc, scale, and size. Loc is the center of the distribution. Scale is the standard deviation of the distribution and size is the output shape...
It is important to know whether you have a discrete or continuous variable when selecting a distribution to model your data. TheBinomialandPoissondistributions are popular choices for discrete data while theGaussianandLognormalare popular choices for continuous data. Test your understanding of Discrete ...
Which i expect. When i do adaptive thresholding with : adaptiveThreshold(image, image,255,ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY,15,-5); i get : Which looks like edge detection and not thresholding. What i expected was black and white areas . So my question is, why does t...
Linear regression assumes gaussian (or normal) distribution of dependent variable. Logistic regression assumes binomial distribution of dependent variable. Share Improve this answer Follow answered May 23, 2020 at 15:11 Sandeep R 111 bronze badge Add a comment 0 The basic difference betw...
normal distributed, then you would have to choose some other measure of uncertainty. so when it comes to uncertainties there is no one-size-fits-all solution. however, gaussian error propagation based on standard deviations is the go-to if there are no reasons against it and in t...
This question shows research effort; it is useful and clear 4 Save this question. Show activity on this post. what is the difference between Poisson distribution as an approximation of Binomial distribution and Normal (Gaussian) distribution as an approximation of Binomial distribution? Both are ...
In this paper, a novel edge-based active contour method is proposed based on the difference of Gaussians (DoG) to segment intensity inhomogeneous images. DoG is known as a feature enhancement tool, which can enhance the edges of an image. However, in the proposed energy functional it is use...
Then we use the linear fitting method to get the relationship between the two fracture weaknesses, and change the variables to precondition the inversion problem. The Bayesian framework, under the hypothesis of a Cauchy distribution prior information and a Gaussian distribution likeliho...