• First we will generate 2000 points from two different multivariate Gaussian distributions.
• Then we will generate a 2d histogram of this data and render in 3D the possible approximation of the Gaussian, a result that we could have achieved from a Gaussian Mixture Model.

simulation.g1 <- mvrnorm(n = 1000, c(0,0), diag(2))
simulation.g2 <- mvrnorm(n = 1000, c(-3,-3), diag(c(0.3,10)))
simulation <- rbind(simulation.g1, simulation.g2)
qtd <- kde2d(simulation[,1], simulation[,2])

plot_ly(x = qtd$x, y = qtd$y, z = qtd$z) %>% add_surface()