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Brooks, S., Gelman, A. Ogólne metody monitorowania zbieżności
Iteracyjne
Symulacje,
1998 r.
Chen, A. Chan, D., Koehler, J., Wang, Y., Nie, Y, Jin, Y., Perry, M.,
Google, Inc. Korekta uprzedzeń w przypadku płatnego wyszukiwania w media miksie
Modelowanie,
2018 r.
Clark, Michael. Podstawy Bayesa: wprowadzenie z zakresu zastosowania
w R i
Stan.
z Uniwersytetu Michigan. (11.09.2015).
Gelman, A. Rubin, D., Wniosek z symulacji iteracyjnej przy użyciu wielu
Sekwencje,
1992 r.
Hern ́an, Massachusetts, Robins JM (2020). Wnioskowanie przyczynowe: co
Jeśli: Boca
Raton: Chapman Hall/CRC.
Jin, Y., Wang, Y., Nie, Y, Chan, D., Koehler, J., Google Inc. Bayesian
Metody modelowania media miksu z uwzględnieniem przenoszenia i kształtu
Efekty
2017 r.
Ng, E., Wang, Z., & Dai, A. Bayesowski model współczynników zmiennych czasu z
Zastosowania do modelowania Marketing Miksu,
2021 r.
Pearl, Judea. Przyczyną. Cambridge University Press. (2009-09-14) ISBN
9781139643986.
Spline (matematyka),
Wikipedii.
Nie, Y, Wang, Y., Jin, Y., Chan, D., Koehler, J., Google Inc. Na poziomie geograficznym
Bayesowski hierarchiczny media miks
Modelowanie
2017 r.
prawdopodobieństwo Tensorflow.
Wang, Y., Jin, Y., Nie, Y, Chan, D., Koehler, J., Google Inc. A
Hierarchiczne podejście Bayesowskie do udoskonalania modeli media miksu za pomocą kategorii
Dane,
2017 r.
Zhang, Y. Wurm, M., Li, E., Wakim, A., Kelly, J., Price, B., Liu, Y.,
Google Inc. Kalibracja modelu media miksu z Bayesianem
Poprzednie edycje
2023 r.
Zhang, Y. Wurm, M., Wakim, A., Li, E., Liu, Y., Google Inc. Bayesian
Hierarchiczny model media miksu z uwzględnieniem zasięgu i częstotliwości
Dane
2023 r.
O ile nie stwierdzono inaczej, treść tej strony jest objęta licencją Creative Commons – uznanie autorstwa 4.0, a fragmenty kodu są dostępne na licencji Apache 2.0. Szczegółowe informacje na ten temat zawierają zasady dotyczące witryny Google Developers. Java jest zastrzeżonym znakiem towarowym firmy Oracle i jej podmiotów stowarzyszonych.
Ostatnia aktualizacja: 2024-09-05 UTC.
[null,null,["Ostatnia aktualizacja: 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."]]
