作者: Nebulizer 時間: 2025-3-21 21:50 作者: Haphazard 時間: 2025-3-22 01:32 作者: Confirm 時間: 2025-3-22 07:25
978-3-8349-2749-1Gabler Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011作者: dysphagia 時間: 2025-3-22 10:21 作者: Meager 時間: 2025-3-22 14:41
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.作者: SOW 時間: 2025-3-22 19:33 作者: 積云 時間: 2025-3-22 22:03 作者: 危險 時間: 2025-3-23 05:12 作者: 連鎖 時間: 2025-3-23 08:15 作者: 傻 時間: 2025-3-23 11:14 作者: Coordinate 時間: 2025-3-23 16:37 作者: 欺騙手段 時間: 2025-3-23 19:58 作者: Original 時間: 2025-3-23 23:37 作者: irreparable 時間: 2025-3-24 02:30 作者: 休閑 時間: 2025-3-24 10:22
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.作者: COWER 時間: 2025-3-24 11:16 作者: Immunotherapy 時間: 2025-3-24 16:03
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作者: crease 時間: 2025-3-24 22:41
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.作者: heartburn 時間: 2025-3-24 23:18 作者: 兩棲動物 時間: 2025-3-25 07:14
Discrete Customer Choice Analysisow-cost travel market that is based on real-world data. Following many other works on choice analysis (see below Table 9.1), the method of discrete customer choice analysis is employed here to model and understand customer purchasing behavior at a disaggregated individual decision maker level.作者: Acquired 時間: 2025-3-25 10:38 作者: 兒童 時間: 2025-3-25 11:40
Computational Results and Evaluatione first Section 12.1. Additionally, specific elasticities and substitutional patterns are inferred in Section 12.2, and finally Section 12.3 draws targeted conclusions on the usage of the reported results in actual airfare pricing.作者: Minutes 時間: 2025-3-25 18:32 作者: 現(xiàn)實 時間: 2025-3-25 21:27 作者: CYT 時間: 2025-3-26 03:22
Book 2011heoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity. He proves that the development of the necessary forecasting models is indeed possible, i.e., through the usage of rea作者: 現(xiàn)任者 時間: 2025-3-26 04:56
olatile markets. Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity. He proves that the development of the necessary forecasting models is inde作者: neurologist 時間: 2025-3-26 11:40
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