# Baba Brinkman
Populaarseimad podcastide jaod
Uuendatud:
Dirk Murray Brinkman (born October 22, 1978) is a Canadian rapper and playwright best known for recordings and performances that combine hip hop music with literature, theatre, and science.
== Early life and education ==
Born in the remote community of Riondel, British Columbia, in a log cabin built by his parents, Brinkman is the eldest of three children of Joyce Murray, a Member of the Parliament of Canada, and Dirk Brinkman, Sr., who is notable for having founded the world's only private company responsible for planting more than one billion trees. Dirk Sr gave Brinkman the honorific nickname "Baba" at birth, because of his son's contemplative, Buddha-like expression.
Bayesian Statistics vs. Epistemology
Today's clip is from episode 160, featuring Vaden Masrani. In this conversation, Vaden explores the tension between Bayesian statistics and Bayesian epistemology, and why he sees them as fundamentally different.He explains why Bayesian epistemology can run into problems when trying to explain where…
The Future of Faster MCMC
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…
Making Gaussian Processes Easier to Use
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…
#164 Bayesian Workflow, with Andrew Gelman, Aki Vehtari & Richard McElreath
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 covers w…
Why a Bayesian Workflow Goes Beyond Fitting Models
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 and…
Bayesian Principal Stratification: Modeling Treatment Effects
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 observed.
…
#165 Hierarchical Sequential Sampling Modeling, with Alex Fengler
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 hierarchical …
How AI Can Evaluate Bayesian Workflows with Bayesify
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…
#166 PTGP: A New Gaussian Process Library, with Bill Engels & Jesse Grabowski
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 Gaussian p…
What Do Kernels, Length Scale & Amplitude Mean in Gaussian Processes?
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 quick…
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