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Fonnesbeck & Wiecki- Probabilistic Programming and Bayesian Computing with PyMC | PyData London 2024
Probabilistic programming with PyMC enables flexible modeling of complex systems, while Bayesian computing provides a powerful framework for inference and prediction.
- Probabilistic programming with PyMC allows for flexible modeling of complex systems.
- Data engineering is a crucial step in Bayesian modeling, and missing data can impact the model’s performance.
- The R hat statistic can be used to check for convergence of the Markov Chain Monte Carlo (MCMC) algorithm.
- The posterior distribution provides a probabilistic inference of the unknown parameters.
- The MMCMC algorithm uses multiple chains to improve the convergence and estimation of the posterior distribution.
- The PyMC library has a variety of samplers available, including the NUTS algorithm, which is suitable for high-dimensional problems.
- The Laplace distribution can be used as a prior distribution for variables with heavy-tailed distributions.
- The Gaussian distribution is often used as a prior distribution for variables with normal distributions.
- Controlling the number of iterations and the number of chains can improve the convergence and estimation of the posterior distribution.
- The posterior distribution provides a probabilistic summary of the unknown parameters, which can be used to make predictions and make inferences about the system.
- The PMCMC algorithm can be used for Bayesian modeling with complex systems, and it is particularly useful when the posterior distribution is difficult to sample from.
- The NUTS algorithm can be used as a global sampler for high-dimensional problems, and it is particularly useful when the posterior distribution is multimodal.
- PyMC provides a variety of tools and libraries for Bayesian modeling, including the PyMC library and the RVs library.
- Bayesian modeling with PyMC provides a flexible and powerful framework for analyzing complex systems, and it can be used for a wide range of applications, including marketing, finance, and healthcare.
- The PyMC library provides a variety of tools and libraries for Bayesian modeling, including the PMCMC algorithm, the NUTS algorithm, and the Laplace distribution.
- The RVs library provides a variety of tools and libraries for Bayesian modeling, including the RVs library and the PyMC library.
- Bayesian modeling with PyMC provides a flexible and powerful framework for analyzing complex systems, and it can be used for a wide range of applications, including marketing, finance, and healthcare.
- The PMCMC algorithm can be used to estimate the parameters of a complex system, and it can be used to make predictions about the system.
- The NUTS algorithm can be used as a global sampler for high-dimensional problems, and it can be used to estimate the parameters of a complex system.
- The Laplace distribution can be used as a prior distribution for variables with heavy-tailed distributions, and it can be used to estimate the parameters of a complex system.
- The PyMC library provides a variety of tools and libraries for Bayesian modeling, including the PMCMC algorithm, the NUTS algorithm, and the Laplace distribution.
- Bayesian modeling with PyMC provides a flexible and powerful framework for analyzing complex systems, and it can be used for a wide range of applications, including marketing, finance, and healthcare.