Brooks, S.、Gelman, A.、一般監控方式 疊代 模擬 1998 年。
Chen, A.、Chan, D.、Koehler, J.、Wang, Y.、Sun, Y.、Jin, Y.、Perry, M.、 Google, Inc. Bias 更正適用於媒體組合中的付費搜尋廣告 模擬 2018 年。
Clark, Michael,貝葉斯基本概念:介紹應用程式的概念簡介 在 R 和 Stan: 密西根大學。(2015 年 9 月 11 日)。
Gelman, A.、Rubin, D.、使用多個疊代模擬推論 序列 1992 年。
Hern ‘an MA, Robins JM (2020 年),因果推論:內容 如果:博卡 賴頓:查普曼與大廳/CRC。
Jin, Y.、Wang, Y.、Sun, Y.、Chan, D.、Koehler, J.、Google Inc. 貝葉斯文 使用 Carryover 和 Shape 建立媒體組合模型的方法 特效 2017。
Ng, E.、Wang, Z.、&Dai, A.貝葉斯時間變化了係數模型: 應用程式到行銷組合模式分析, 2021 年。
珍珠白,Juda。因果關係。劍橋大學出版社。(2009-09-14) 國際標準書號 9781139643986。
Spline (數學), Wikipedia。
Sun, Y.、Wang, Y.、Jin, Y.、Chan, D.、Koehler, J.、Google Inc. 地理區域層級 貝葉斯階層式媒體組合 模擬 2017。
Wang, Y.、Jin, Y.、Sun, Y.、Chan, D.、Koehler, J.、Google LLC 按照階層式貝葉斯做法,利用類別改善媒體組合模型 資料 2017。
Zhang, Y.、Wurm, M.、Li, E.、Wakim, A.、Kelly, J.、Price, B.、Liu, Y.、 Google Inc. 與貝葉斯的媒體組合校正模型 先前 2023 年。
Zhang, Y.、Wurm, M.、Wakim, A.、Li, E.、Liu, Y.、Google Inc. 貝葉斯文 階層式媒體組合模型,結合觸及數與展示頻率 資料 2023 年。
參考資料
除非另有註明,否則本頁面中的內容是採用創用 CC 姓名標示 4.0 授權,程式碼範例則為阿帕契 2.0 授權。詳情請參閱《Google Developers 網站政策》。Java 是 Oracle 和/或其關聯企業的註冊商標。
上次更新時間:2024-09-05 (世界標準時間)。
[null,null,["上次更新時間:2024-09-05 (世界標準時間)。"],[[["\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."]]