#pymc نتائج البحث
You’ve been saying you’ll learn #Bayesian modeling someday. Make someday 𝐭𝐨𝐝𝐚𝐲. The next 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩 from #PyMC Labs begins October 6 and registration closes this week. Secure your spot now: dub.link/ngodkW9
Can AI run your marketing engine? Join #PyMC Labs, Plan.Net Group & THE MARCOM ENGINE at #InnoDay25 to explore how Bayesian agents orchestrate the entire marketing value chain — turning marketing into a measurable growth engine. 🔗 dub.link/w9cogEJ
書籍「RとStanではじめるベイズ統計モデリングによるデータ分析入門」をPythonとPyMCとBambiで。 第５部 状態空間モデルに突入！ ローカルレベルモデルはうまく行きました！ この先、AR()とGaussianRandomWalk()で乗り切れるか、ちょっと心配😅 （続く） #のんびり統計 #PyMC #ベイズモデリング
Not a fan of #PyMC's `with pm.Model()` context API? Interesting new API idea for a function based API by @zaxtax. Implementation is just a few lines of code and it integrates seamlessly with the rest of the API. zinkov.com/posts/2023-alt…
What if your next product test didn’t need a human panel? New research by #PyMC Labs & #Colgate-Palmolive shows LLMs can replicate human survey patterns with 90%+ accuracy—no fine-tuning, no panels, no bias. Read the white paper: dub.link/ECczk6S #SyntheticConsumers
#PyMC #PyDataKampala Probabilistic programming in Python confers a number of advantages including; 1. multi-platform compatibility 2. an expressive yet clean and readable syntax 3. easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython
Your model isn’t enough if it can’t be trusted. Learn how to build robust, decision-ready Bayesian models in our live workshop. 🚨 Last chance! Seats close this week. 👉 Save your spot dub.link/pzkZPEL Featuring @AllenDowney (Think Bayes). #BayesianModeling #PyMC
Can LLMs make business decisions you'd actually trust? @twiecki joins @hugobowne to share how #PyMC Labs is building GenAI systems for clients like Colgate 🧪 Real-world use cases 📊 90% accuracy 🔁 Closed-loop systems 🔗 dub.link/genai-decision #GenAI #LLMsystems
Most ML models predict, but don’t show how certain those predictions are. #Bayesian methods with #PyMC change that. Tonight in Montreal, Christopher Fonnesbeck will show how to model uncertainty with practical workflows + live coding. 🔗 dub.link/YoRz04P
𝐎𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 𝐠𝐫𝐨𝐰𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 𝐰𝐡𝐞𝐧 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. Join Rob Zinkov at @pydataamsterdam 10th Anniversary on Sep 26, 1:20 PM for a beginner-friendly #PyMC sprint. Learn, contribute, and connect with the community! #PyTensor
Our Applied #Bayesian Modeling Workshop - October cohort is now open! After incredible feedback from August, we're running it again, starting Oct 6th. Learn directly from the creators of #PyMC in 4 weeks of hands-on training! Register before spots fill: dub.link/QWC7F2N
Beautiful feeling: reproducing the same modelling results as a paper (direct.mit.edu/opmi/article/d…) with a totally independent implementation (in #pymc) based just on the description in the text (first: paper, second: mine)
Convenient Bayesian Marketing Mix Modeling with #PyMC Marketing towardsdatascience.com/convenient-bay… by @robert_kubler
Stop waiting weeks for modeling answers! Our new Expert Access Program (EAP) gives your team direct access to our #Bayesian and #PyMC experts for timely answers, guidance, and confidence to move faster. Learn more👉 dub.link/BAbeiyf
朝活でMCMCが収束しなかった問題・・・ お昼休憩に珈琲を飲みながら、色々と試してたら、見事に解決できた😆✌️ 二項分布を使って、観測しきれなかった「真の観測数を推論」するというアプローチ、非常に面白いですね🤔 時系列なのも僕好み(笑) 詳細は・・・ ↓ #Python #ベイズ #PyMC #データ分析
「StanとRでベイズ統計モデリング」ですが、 RとStanは興味ないので、PyMC5で翻訳しながら進めているのですが・・・ Rって便利そうですね🤔 PyMC5だと、こんな風にカテゴリ変数でソートして、描画の時にはスライシングしないと・・・ こんな比較描画が一発でできない😩 #Python #PyMC #ベイズ統計
Deadlines, complex models, new approaches... even strong teams hit roadblocks Our Expert Access Program (EAP) gives access to #PyMC and #Bayesian experts like Niall Oulton to solve tricky models, validate approaches, and strengthen your team. Learn more: dub.link/0wtmrRX
Introducing AthlyticZ Live Interactive instruction on our custom LMS with industry experts. First up: PyMC expert Alex Andorra leading sports-data case studies. Learn live and keep progressing on-platform: athlyticz.com/cohorts/alex-a… #python #PyMC #sportsanalytics
Can AI run your marketing engine? Join #PyMC Labs, Plan.Net Group & THE MARCOM ENGINE at #InnoDay25 to explore how Bayesian agents orchestrate the entire marketing value chain — turning marketing into a measurable growth engine. 🔗 dub.link/w9cogEJ
What if your next product test didn’t need a human panel? New research by #PyMC Labs & #Colgate-Palmolive shows LLMs can replicate human survey patterns with 90%+ accuracy—no fine-tuning, no panels, no bias. Read the white paper: dub.link/ECczk6S #SyntheticConsumers
Can #LLMs really estimate prices, and why does it matter for business? At #PyMC Labs, we built a “Price is Right” inspired benchmark testing models’ price knowledge & strategy. Results show accuracy isn’t enough; reasoning matters. Explore 👉dub.link/eSu9sVm
たのしいベイズモデリング２が楽しい！ 第18章は項目反応ツリーモデル。 項目反応理論に様々なモデルがあるのですね！ brmsによるモデリングのPyMC5化にはBambiを利用。 テキストと結果が合わず１日苦戦… マッピング行列での変換処理で目的変数がobject型になってたｗ #のんびり統計 #ベイズ #PyMC
書籍「RとStanではじめるベイズ統計モデリングによるデータ分析入門」をPythonとPyMCとBambiで。 第５部 状態空間モデルに突入！ ローカルレベルモデルはうまく行きました！ この先、AR()とGaussianRandomWalk()で乗り切れるか、ちょっと心配😅 （続く） #のんびり統計 #PyMC #ベイズモデリング
Convenient Bayesian Marketing Mix Modeling with #PyMC Marketing towardsdatascience.com/convenient-bay… by @robert_kubler
Can AI run your marketing engine? Join #PyMC Labs, Plan.Net Group & THE MARCOM ENGINE at #InnoDay25 to explore how Bayesian agents orchestrate the entire marketing value chain — turning marketing into a measurable growth engine. 🔗 dub.link/w9cogEJ
Not a fan of #PyMC's `with pm.Model()` context API? Interesting new API idea for a function based API by @zaxtax. Implementation is just a few lines of code and it integrates seamlessly with the rest of the API. zinkov.com/posts/2023-alt…
Most ML models predict, but don’t show how certain those predictions are. #Bayesian methods with #PyMC change that. Tonight in Montreal, Christopher Fonnesbeck will show how to model uncertainty with practical workflows + live coding. 🔗 dub.link/YoRz04P
Our Applied #Bayesian Modeling Workshop - October cohort is now open! After incredible feedback from August, we're running it again, starting Oct 6th. Learn directly from the creators of #PyMC in 4 weeks of hands-on training! Register before spots fill: dub.link/QWC7F2N
「StanとRでベイズ統計モデリング」ですが、 RとStanは興味ないので、PyMC5で翻訳しながら進めているのですが・・・ Rって便利そうですね🤔 PyMC5だと、こんな風にカテゴリ変数でソートして、描画の時にはスライシングしないと・・・ こんな比較描画が一発でできない😩 #Python #PyMC #ベイズ統計
ちなみにマッピング行列での変換処理は・・・ ①画像：分析データ ②画像：マッピング行列 ③画像：変換関数（Rのdendrify関数相当を手組み。ループだらけ😎） ④画像：変換処理・変換後データ ③の整数化漏れに気づくのに１日かかりました。 #のんびり統計 #ベイズ #PyMC
📉 Market data is messy. Traditional models assume too much. With PyMC, finance teams can: ✅ Quantify uncertainty ✅ Handle skewed, fat-tailed data ✅ Improve VaR, pricing & more 👉 dub.link/eonSuxd 📬 Need help applying this? DM us or email [email protected] #pymc
GW中の目標の１つ「たのしいベイズモデリング２ブログの原稿完成」を達成しました！ 超うれし✨️ 執筆の先生方の熱意・真剣さ・愛情にやられています。 ありがとうございました。 さて次は？ 因果推論を往復、反実仮想機械学習に進む、異常検知ブログを描く… #のんびり統計 #ベイズ #PyMC
Deadlines, complex models, new approaches... even strong teams hit roadblocks Our Expert Access Program (EAP) gives access to #PyMC and #Bayesian experts like Niall Oulton to solve tricky models, validate approaches, and strengthen your team. Learn more: dub.link/0wtmrRX
Can LLMs make business decisions you'd actually trust? @twiecki joins @hugobowne to share how #PyMC Labs is building GenAI systems for clients like Colgate 🧪 Real-world use cases 📊 90% accuracy 🔁 Closed-loop systems 🔗 dub.link/genai-decision #GenAI #LLMsystems
Flexible Cohort Retention Analysis with BART in #PyMC Instead of a linear model to estimate the retention rate, uses a flexible non-parametric model (BART) that estimates complex relationships between response and predictors. juanitorduz.github.io/retention_bart/ by @juanitorduz @pymc_devs
朝活でMCMCが収束しなかった問題・・・ お昼休憩に珈琲を飲みながら、色々と試してたら、見事に解決できた😆✌️ 二項分布を使って、観測しきれなかった「真の観測数を推論」するというアプローチ、非常に面白いですね🤔 時系列なのも僕好み(笑) 詳細は・・・ ↓ #Python #ベイズ #PyMC #データ分析
たのしいベイズモデリング２の１周目完了！ 最終19章は収束するも事後分布統計量がテキストと合致しませんでした（汗） 添付の箱ひげ図は、介入前（青）と介入後（オレンジ）の目的変数の確認用です。 年明けから２周目＆ブログ化しようと思います。 #のんびり統計 #ベイズモデリング #PyMC
Great to see causal inference being included via a new "do" operator in #pymc #Python. github.com/pymc-labs/rese…
🔥 PyMC-Marketing release is live! → No repeat transactions? Bring covariates for better predictions. → Zero-spend channels breaking your MMM? Fixed. → Scaled channel contributions now computed post-sampling — faster & nutpie-ready! 🔗 dub.link/pymc_marketing #PyMC #MMM
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