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## How do I create a complex Gaussian joint PDF plot (example figure)?

I assume, by Gaussian equation, you mean the probability density function of a gaussian distribution. It is given as where, \sigma is standard distribution, \mu is average and \pi is constant. Figure below shows a gaussian distribution assuming \mu as 0 and \sigma as 1. Reference. Google Colaboratory notebook on Gaussian Distribution Gaussian distribution. Why is it important in data science and machine learning?

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## Create PDF: All You Need to Know

What kind of sample and what kind of regression is used in classification studies are both important in machine learning or data science. One of the most common regression techniques used in data science is logistic regression. It assumes that we have \(\mu\) (average) and \(\sigma\) (standard deviation) distribution. The sample are random numbers. We see that the logistic regression equation is \[\int_{\Delta t}\mu \math rm{d}\sigma = \int_{t \GEQ t_{1 + d}}2\beta \math rm {d}\sigma, \] where \(\Delta t\GEQ t_{1 + d}}\), \(\beta \GEQ 0\), and \(\Delta t = -t_{1 + d} / log(t)\) and \(\math rm {d}\) is the number of classes for the data sets. The formula assumes \(\exp{- \beta sigma \Delta t + \sigma}_{1 + t} \) which is the average value in a logistic regression. It is also important to know the variance, or standard deviation, of the distribution and if they are significant. They are not so.