Referensi
Tetap teratur dengan koleksi
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Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2024-09-05 UTC.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2024-09-05 UTC."],[[["This compilation of resources focuses on Bayesian methods and their applications, particularly in media mix modeling (MMM) for marketing analysis."],["Several resources explore the use of Bayesian hierarchical models, incorporating factors like carryover effects, shape effects, geographic variations, and category data to enhance MMM accuracy."],["The list also includes foundational materials on Bayesian statistics, causal inference, and convergence diagnostics for iterative simulations, providing a comprehensive understanding of the underlying concepts."],["Resources 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."],["This collection serves as a valuable guide for researchers and practitioners seeking to leverage Bayesian techniques for advanced marketing measurement and decision-making."]]],["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"]]
