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Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2024-09-05 UTC.
[null,null,["Ultimo aggiornamento 2024-09-05 UTC."],[[["\u003cp\u003eThis compilation of resources focuses on Bayesian methods and their applications, particularly in media mix modeling (MMM) for marketing analysis.\u003c/p\u003e\n"],["\u003cp\u003eSeveral resources explore the use of Bayesian hierarchical models, incorporating factors like carryover effects, shape effects, geographic variations, and category data to enhance MMM accuracy.\u003c/p\u003e\n"],["\u003cp\u003eThe list also includes foundational materials on Bayesian statistics, causal inference, and convergence diagnostics for iterative simulations, providing a comprehensive understanding of the underlying concepts.\u003c/p\u003e\n"],["\u003cp\u003eResources from Google researchers showcase cutting-edge advancements in MMM, including bias correction for paid search, the integration of reach and frequency data, and the utilization of Bayesian priors for model calibration.\u003c/p\u003e\n"],["\u003cp\u003eThis collection serves as a valuable guide for researchers and practitioners seeking to leverage Bayesian techniques for advanced marketing measurement and decision-making.\u003c/p\u003e\n"]]],["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,["# References\n\n- [Bias--variance tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff),\n Wikipedia.\n\n- Brooks, S., Gelman, A., [General Methods for Monitoring Convergence of\n Iterative\n Simulations](https://www2.stat.duke.edu/%7Escs/Courses/Stat376/Papers/ConvergeDiagnostics/BrooksGelman.pdf),\n 1998.\n\n- Chen, A., Chan, D., Koehler, J., Wang, Y., Sun, Y., Jin, Y., Perry, M.,\n Google, Inc. [Bias Correction For Paid Search In Media Mix\n Modeling](https://research.google/pubs/bias-correction-for-paid-search-in-media-mix-modeling/),\n 2018.\n\n- Clark, Michael. [Bayesian Basics: A conceptual Introduction with application\n in R and\n Stan](https://civil.colorado.edu/%7Ebalajir/CVEN6833/bayes-resources/Intro2Bayes.pdf).\n University of Michigan. (2015-09-11).\n\n- Gelman, A., Goodrich, B., Gabry, J., Vehtari, A., [R-squared for Bayesian\n regression models](https://sites.stat.columbia.edu/gelman/research/published/bayes_R2_v3.pdf),\n 2018.\n\n- Gelman, A., Rubin, D., [Inference from Iterative Simulation Using Multiple\n Sequences](http://www.stat.columbia.edu/%7Egelman/research/published/itsim.pdf),\n 1992.\n\n- Hernán MA, Robins JM (2020). [Causal Inference: What\n If](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book). Boca\n Raton: Chapman \\& Hall/CRC.\n\n- Jin, Y., Wang, Y., Sun, Y., Chan, D., Koehler, J., Google Inc. [Bayesian\n Methods for Media Mix Modeling with Carryover and Shape\n Effects](https://research.google/pubs/bayesian-methods-for-media-mix-modeling-with-carryover-and-shape-effects/)\n 2017.\n\n- Ng, E., Wang, Z., \\& Dai, A. [Bayesian Time Varying Coefficient Model with\n Applications to Marketing Mix Modeling](https://arxiv.org/abs/2106.03322),\n 2021.\n\n- Pearl, Judea. Causality. Cambridge University Press. (2009-09-14) [ISBN\n 9781139643986](https://isbnsearch.org/isbn/9781139643986).\n\n- [Spline (mathematics)](https://en.wikipedia.org/wiki/Spline_(mathematics)/),\n Wikipedia.\n\n- Sun, Y., Wang, Y., Jin, Y., Chan, D., Koehler, J., Google Inc. [Geo-level\n Bayesian Hierarchical Media Mix\n Modeling](https://research.google/pubs/geo-level-bayesian-hierarchical-media-mix-modeling/)\n 2017.\n\n- [Tensorflow Probability](https://www.tensorflow.org/probability).\n\n- Wang, Y., Jin, Y., Sun, Y., Chan, D., Koehler, J., Google Inc. [A\n Hierarchical Bayesian Approach to Improve Media Mix Models Using Category\n Data](https://research.google/pubs/a-hierarchical-bayesian-approach-to-improve-media-mix-models-using-category-data/),\n 2017.\n\n- Zhang, Y., Wurm, M., Li, E., Wakim, A., Kelly, J., Price, B., Liu, Y.,\n Google Inc. [Media Mix Model Calibration With Bayesian\n Priors](https://research.google/pubs/media-mix-model-calibration-with-bayesian-priors/)\n 2023.\n\n- Zhang, Y., Wurm, M., Wakim, A., Li, E., Liu, Y., Google Inc. [Bayesian\n Hierarchical Media Mix Model Incorporating Reach and Frequency\n Data](https://research.google/pubs/bayesian-hierarchical-media-mix-model-incorporating-reach-and-frequency-data/)\n 2023."]]
