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Learning Bayesian Statistics

#90, Demystifying MCMC & Variational Inference, with Charles Margossian

Sep 6, 2023
Charles Margossian, computational mathematician, discusses the differences between MCMC and Variational Inference (VI). They explore beginner questions, the practical applications of Bayesian methods in pharmacometrics and epidemiology, and the challenges of fitting mechanistic models in drug absorption and effects. They also touch on nested Laplace approximations and the complexity of Bayesian methods and data analysis.
01:37:36

Podcast summary created with Snipd AI

Quick takeaways

  • Bayesian statistics brings interpretability and handles uncertainty in pharmacometrics, aiding in optimizing treatment regimens based on early data from clinical trials.
  • The use of GPUs in pharmacometrics improves computational efficiency, but challenges remain in solving ODE-based models.

Deep dives

Pharmacometrics and its application to pharmacology and patient treatment

Pharmacometrics is a field that applies quantitative methods to pharmacology, similar to how econometrics applies quantitative methods to economics. In pharmacometrics, the focus is on modeling the pharmacokinetics (absorption, diffusion) and pharmacodynamics (effect on the body) of drugs, as well as understanding dosing regimens and their effects. Pharmacometric models aim to predict outcomes and optimize treatments based on early data from clinical trials, taking into account factors like drug administration, dosage, and patient factors. These models provide a mechanistic understanding of drug behavior and help in exploring different treatment regimens. Bayesian statistics brings valuable contributions to pharmacometrics by allowing for interpretable mechanistic models, handling uncertainty, exploring parameter space, and incorporating heterogeneity among patients through hierarchical modeling. However, challenges remain in terms of computational efficiency, model complexity, and handling non-linear models, especially for cases involving ODE-based models.

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