Generating Simulated Data Points That Follow a Give Probability Density Function

Generating Simulated Data Points That Follow a Give Probability Density Function

Generating Simulated Data Points That Follow a Give Probability Density Function. This post combines a few basic techniques in order to generate some simulated data that follow the distribution of a given probability density function (p.d.f).

During the last couple of weeks, I’ve been simulating some data for a statistical inference project. While the topic of simulating data is quite broad and depends on the application, I thought that a more general approach of simulating data points that follow a particular probability distribution may be useful to those working on scientific experiments and mathematical modeling.

This post combines a few basic techniques in order to generate some simulated data that follow the distribution of a given probability density function (p.d.f).

Let’s assume that we have a p.d.f of the form,

def pdf(x):
        F = np.exp(-x**2/2)
        return F

Plotting this to verify,

x = np.linspace(-8,8,100)
    plt.plot(x,pdf(x))

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