Listen Score
LS 41
Global Rank
TOP 1.5%

이 팟캐스트 소개 🔗

진행자:
업데이트 빈도:
every 6 days
평균 오디오 길이:
57 minutes
게스트 인터뷰
스폰서 있음
영어
미국
에피소드 222 개
2019년 9월 20일부터
episodic

AUDIENCE OF THIS PODCAST 🔗

~54.82% of listeners are from United States.
🇺🇸
US
54.82%
🇬🇧
GB
15.32%
🇨🇦
CA
7.79%
🇩🇪
DE
3.17%
🇧🇷
BR
1.71%
🇷🇴
RO
1.58%
🇪🇸
ES
1.45%
🇫🇮
FI
1.32%
🇮🇳
IN
1.18%
🇫🇷
FR
1.18%
🇦🇺
AU
1.05%
🇦🇷
AR
0.92%
🇸🇪
SE
0.79%
🇮🇹
IT
0.79%
🇰🇷
KR
0.66%
🇨🇿
CZ
0.66%
🇿🇦
ZA
0.66%
🇱🇺
LU
0.66%
🇧🇬
BG
0.52%
🇳🇱
NL
0.39%
🇦🇹
AT
0.39%
🇲🇽
MX
0.26%
🇳🇿
NZ
0.26%
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BE
0.26%
🇽🇰
XK
0.26%
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KE
0.13%
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IE
0.13%
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SD
0.13%
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TW
0.13%
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TR
0.13%
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SA
0.13%
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UA
0.13%
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ET
0.13%
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SG
0.13%
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NG
0.13%
🇨🇱
CL
0.13%
🇮🇶
IQ
0.13%
🇵🇦
PA
0.13%
🇳🇴
NO
0.13%
Others
0.13%
* Data source: directly measured on Listen Notes. 실시간

최신 에피소드 🔗

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…

과거 에피소드 검색

Learning Bayesian Statistics의 과거 에피소드를 검색하세요.

이전 에피소드

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…
면책 조항: 이 페이지에 포함된 팟캐스트와 작품은 Alexandre Andorra에서 가져온 것입니다. 이 팟캐스트는 소유자의 재산이며 Listen Notes, Inc.와 제휴하거나 보증하지 않습니다.