Listen Score
LS 41
Global Rank
TOP 1.5%

關於這個 Podcast 🔗

主持人:
更新頻率:
every 6 days
平均音訊長度:
57 minutes
嘉賓訪談
有贊助商
英語
美國
222 集
自從 2019年9月20日
episodic

AUDIENCE OF THIS PODCAST 🔗

~54.82% of listeners are from United States.
🇺🇸
US
54.82%
🇬🇧
GB
15.32%
🇨🇦
CA
7.79%
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DE
3.17%
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BR
1.71%
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RO
1.58%
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ES
1.45%
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FI
1.32%
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IN
1.18%
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FR
1.18%
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AU
1.05%
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AR
0.92%
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SE
0.79%
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IT
0.79%
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KR
0.66%
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CZ
0.66%
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ZA
0.66%
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LU
0.66%
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BG
0.52%
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NL
0.39%
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AT
0.39%
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MX
0.26%
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NZ
0.26%
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BE
0.26%
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0.26%
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0.13%
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0.13%
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SD
0.13%
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0.13%
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0.13%
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SA
0.13%
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UA
0.13%
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0.13%
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SG
0.13%
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NG
0.13%
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CL
0.13%
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IQ
0.13%
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PA
0.13%
🇳🇴
NO
0.13%
Others
0.13%
* Data source: directly measured on Listen Notes. 即時

最新一集 🔗

Today's clip is from Episode 166, featuring Bill Engels & Jesse Grabowski. In this conversation, Bill explains what a kernel means in a Gaussian process and how it defines the idea of similarity between data points. He then breaks down the role of the length scale, showing how it controls how q…

搜尋以前的節目

搜尋Learning Bayesian Statistics以前的節目。

以前的節目

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work! Takeaways: Q: What is PTGP and why did Bill and Jesse build it?A: PTGP is a new Gaussi…
Today's clip is from Episode 165, featuring Alex Fengler. In this conversation, Alex introduces Bayesify , a tool that uses AI to analyze research papers and assess how well they follow a Bayesian workflow.He explains how the tool breaks an analysis down step by step, identifies strengths and weakn…
Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work! Takeaways: Q: What is HSSM and how does it relate to HDDM?A: HSSM stands for hierarchi…
Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains how Bayesian principal stratification can be used to reason about treatment effects when there is an intermediate treatment or outcome that is only partially observe…
Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains why a Bayesian workflow goes far beyond simply fitting a model. He discusses the importance of building, fitting, and checking models, and why moving between simpler…
Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work Takeaways:Q: What is the "Bayesian Workflow" book about, and who is it for? A: It cove…
Today's clip is from Episode 154, featuring Thomas Pinder. In this conversation, Thomas shares what he sees as the next steps for GPJax and how the project could become easier to use beyond its original research-focused audience.He discusses creating a higher-level interface that could make fitting…
Today's clip is from Episode 163, featuring Eliot Carlson and Adrian Seyboldt. In this conversation, Eliot and Adrian look beyond current approaches to HMC adaptation and preconditioning and share the ideas they're most excited to explore next.Eliot discusses new ways of parallelizing MCMC by solvi…
Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work Takeaways:Q: What is mass matrix adaptation, in plain terms?A: Mass matrix adaptation i…
免責聲明: 本頁面嵌入的 Podcast 和封面圖片來自 Alexandre Andorra,歸其所有者所有,與 Listen Notes, Inc. 無關聯,也未獲得 Listen Notes, Inc. 背書。