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BITESIZE | Exploring Dynamic Regression Models, with David Kohns

15 min•18 juni 2025

Today’s clip is from episode 134 of the podcast, with David Kohns.

Alex and David discuss the future of probabilistic programming, focusing on advancements in time series modeling, model selection, and the integration of AI in prior elicitation.

The discussion highlights the importance of setting appropriate priors, the challenges of computational workflows, and the potential of normalizing flows to enhance Bayesian inference.

Get the full discussion here.

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Transcript

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Fler avsnitt av Learning Bayesian Statistics

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#153 The Neuroscience of Philanthropy, with Cherian Koshy

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Bitesize | How To Model Risk Aversion In Pricing?

4 mars•4 min

#152 A Bayesian decision theory workflow, with Daniel Saunders

26 feb.•1 tim 19 min

BITESIZE | How Do Diffusion Models Work?

19 feb.•4 min

#151 Diffusion Models in Python, a Live Demo with Jonas Arruda

12 feb.•1 tim 36 min

#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

28 jan.•1 tim 20 min

BITESIZE | Building Resilience in Modern Tech Careers

21 jan.•25 min

#149 The Future of Work in Tech, with Alana Karen

14 jan.•1 tim 33 min

BITESIZE | The Trial Design That Learns in Real Time

7 jan.•22 min

Learning Bayesian Statistics med Alexandre Andorra finns tillgänglig på flera plattformar. Informationen på denna sida kommer från offentliga podd-flöden.