The expected value and variance are embedded in the PDF of Normal distribution. The expected value is the mean, which is µ, and the variance is the square of standard deviation, σ². To simulate the Normal distribution, we can use the Numpy function: np.random.normal(mu, sigma, 1000) The Normal Distribution is one of the most important distributions. It is also called the Gaussian Distribution after the German mathematician Carl Friedrich Gauss. It fits the probability distribution of many events, eg. IQ Scores, Heartbeat etc. Use the random.normal () method to get a Normal Data Distribution. loc - (Mean) where the peak of The Continuous Ranked Probability Score (CRPS) is a scoring function that compares a single ground-truth value to its predicted distribution. This property makes it relevant to Bayesian machine learning, where models usually output distributional predictions rather than point-wise estimates. It can be viewed as a generalization of the well BElB.