Bias-variance tradeoff, 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. Universidad de Michigan. (2015-09-11).
Gelman, A., Rubin, D., Inference from Iterative Simulation Using Multiple Sequences, 1992.
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
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., y Dai, A. Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling, 2021.
Pearl, Judea. Causality. Cambridge University Press. (2009-09-14) ISBN 9781139643986.
Spline (mathematics), 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.
Referencias
Salvo que se indique lo contrario, el contenido de esta página está sujeto a la licencia Atribución 4.0 de Creative Commons, y los ejemplos de código están sujetos a la licencia Apache 2.0. Para obtener más información, consulta las políticas del sitio de Google Developers. Java es una marca registrada de Oracle o sus afiliados.
Última actualización: 2025-02-19 (UTC)
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Falta la información que necesito","missingTheInformationINeed","thumb-down"],["Muy complicado o demasiados pasos","tooComplicatedTooManySteps","thumb-down"],["Desactualizado","outOfDate","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Problema con las muestras o los códigos","samplesCodeIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 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,[]]