Bias-Varianz-Dilemma, Wikipedia.
Brooks, S., Gelman, A., General Methods for Monitoring Convergence of Iterative Simulations, 1998.
Chen, A., Chan, D., Koehler, J., Wang, Y., Sun, Y., Jin, Y., Perry, M., Google, Inc. Bias Correction For Paid Search In Media Mix Modeling, 2018.
Clark, Michael, Bayesian Basics: A conceptual Introduction with application in R and Stan, University of Michigan, 11. September 2015.
Gelman, A., Rubin, D., Inference from Iterative Simulation Using Multiple Sequences, 1992.
Hernán, M. A., Robins, J. M., Causal Inference: What If, Boca Raton: Chapman & Hall/CRC, 2020.
Jin, Y., Wang, Y., Sun, Y., Chan, D., Koehler, J., Google Inc. Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects, 2017.
Ng, E., Wang, Z. und Dai, A. Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling, 2021.
Pearl, Judea. „Causality“. Cambridge University Press. (14. September 2009) ISBN 9781139643986.
Spline (Mathematik), Wikipedia.
Sun, Y., Wang, Y., Jin, Y., Chan, D., Koehler, J., Google Inc. Geo-level Bayesian Hierarchical Media Mix Modeling, 2017.
Wang, Y., Jin, Y., Sun, Y., Chan, D., Koehler, J., Google Inc. A Hierarchical Bayesian Approach to Improve Media Mix Models Using Category Data, 2017.
Zhang, Y., Wurm, M., Li, E., Wakim, A., Kelly, J., Price, B., Liu, Y., Google Inc. Media Mix Model Calibration With Bayesian Priors, 2023.
Zhang, Y., Wurm, M., Wakim, A., Li, E., Liu, Y., Google Inc. Bayesian Hierarchical Media Mix Model Incorporating Reach and Frequency Data, 2023.
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Zuletzt aktualisiert: 2025-02-19 (UTC).
[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Benötigte Informationen nicht gefunden","missingTheInformationINeed","thumb-down"],["Zu umständlich/zu viele Schritte","tooComplicatedTooManySteps","thumb-down"],["Nicht mehr aktuell","outOfDate","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Problem mit Beispielen/Code","samplesCodeIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],["Zuletzt aktualisiert: 2025-02-19 (UTC)."],[],["The documents cover Bayesian methods and their application in media mix modeling (MMM). Key topics include: bias-variance tradeoff; convergence monitoring for iterative simulations; causal inference; Bayesian hierarchical modeling to improve MMM with category data, reach, frequency, carryover, and shape effects; bias correction for paid search in MMM; and calibration of MMM using Bayesian priors. Splines and TensorFlow Probability are also mentioned, with general bayesian concepts. The work was carried out by researchers in different academic institutions or at google.\n"],null,[]]