The frequentist and the Bayesian perspectives are both valid and useful. As the number of occurrences increase, the measurement of the (in)adequacy of the model improves. Then, through repeated observations, we assess how the model fares when confronted with the real occurrences of the phenomenon. When trying to make quantitative sense of an uncertain phenomenon, the Bayesian perspective starts with a model that directly gives a probability estimate for the phenomenon. The Bayesian perspective reverses the problem. The cross-entropy departs from this perspective by adopting the Bayesian probability perspective. As the frequency rate converges through many experiments, the probability gets estimated more accurately. When trying to make quantitative sense of an uncertain phenomenon, the frequentist perspective states that measurements should be repeated many times, and that by counting the number of occurrences of the phenomenon of interest, it is possible to estimate the frequency of the phenomenon, i.e. Frequentist probability vs Bayesian probabilityĪ common way of understanding statistics is the frequentist probability perspective. This metric departs substantially from the intuition that supports simpler accuracy metrics, like the mean square error or the mean absolute percentage error. From a supply chain perspective, cross-entropy is particularly important as it supports the estimation of models that are also good at capturing the probabilities of rare events, which frequently happen to be the costliest ones. Cross-entropy is of primary importance to modern forecasting systems, because if it is instrumental in making possible the delivery of superior forecasts, even for alternative metrics. The cross-entropy has strong ties with the maximum likelihood estimation. The cross-entropy is a metric that can be used to reflect the accuracy of probabilistic forecasts. Long-term maintenance agreement pricing.Continuous Ranked Probability Score (CRPS).

Quantitative Principles for Supply Chain (Lecture 1.6).21st Century Trends in Supply Chain (Lecture 1.5).Programming Paradigms for Supply Chain (Lecture 1.4).Product-Oriented Delivery for Supply Chain (Lecture 1.3).The Quantitative Supply Chain in a Nutshell (Lecture 1.2).

The Foundations of Supply Chain (Lecture 1.1).

Cross entropy