using a machine learning or statistics technique that assumes your data is normally distributed. Some examples of these includet-tests, ANOVAs, linear regression, linear discriminant analysis (LDA) and Gaussian naive Bayes. (Pro tip: any method with "Gaussian" in the name probably assumes ...
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...
As adjectives the difference between usual and normal is that usual is most commonly occurring while normal is...
Continuous variables that followed a Gaussian distribution are presented as the mean (standard deviation) and were analyzed by t test. The skewed distributed continuous variable was presented as median (interquartile range) and analyzed through the Mann–Whitney U test. U The categorical variable was...
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. ...
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...
Forty-five patients found to have breast cancer and 49 patients who developed breast cancer within 5 years of being screened were paired with normal variants and the thermograms of each group were assessed. No statistically significant separation was resolved between either of the two paired groups...
Hence, we\npropose a moment matching approximation by a normal inverse Gaussian (NIG)\ndistribution and we derive an expression for the asymptotic tail probability.\nNumerical evaluations showed that the NIG approximation matches very well with\nthe exact solution obtained by numerical convolution of...
This study aimed to quantify the difference of Gaussian modelling characteristics derived from radial pulses measured from the three trimesters of healthy pregnant women. Radial pulses were recorded from seventy pregnant women between gestational week 11-13, week 20-22, and then week 37-39. They ...
Although TE2E can work well in both TI-SV and TD-SV, it has several disadvantages. Firstly, TE2E gets a scalar representing the similarity between embedding vector ej and single centroid ck; this makes the network not able to capture features from other enrollment speakers. Therefore, they ...