Titlebook: Operationalizing Dynamic Pricing Models; Bayesian Demand Fore Steffen Christ Book 2011 Gabler Verlag | Springer Fachmedien Wiesbaden GmbH,

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Summary and OutlookThe preceding Chapters 9 – 12 . and finally . a . to understand the particular customer choice probabilities that convert latent demand in . to eventual ., based on the prevalent price environment in the market and the decision makers’ determining characteristics.

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Self-Learning Linear Modelsatent demand based on its characteristics as described in Chapter 5. The presented method rests on the . interpretation of probability, which is fundamentally different from the classical or . interpretation, where probabilities are simply viewed “in terms of the frequencies of random, repeatable ev

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The Demand Forecasting Modelincludes all relevant demand drivers, the model is validated following a typical frequentist interpretation and using the appropriate tests in Section 6.2 before the approach is finally extended to allow for Bayesian learning in Section 6.3.

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