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Automatic differentiation (AD, also called algorithmic differentiation) is an algorithmic approach to automatically evaluate the gradient of functions that are implemented as computer programs.
Automatic differentiation (AD, also called algorithmic differentiation) is an algorithmic approach to automatically evaluate the gradient of functions that are implemented as computer programs.
To solve problems in the real world, one should choose the most appropriate approaches and tools available that are practical to use and hopefully general enough to productise. That said, a Bayesian toolbox can bring benefits by having a consistent framework to model unknown quantities, incorporate prior information, and, depending on the inference engine used, diagnose model fit to data.
To solve problems in the real world, one should choose the most appropriate approaches and tools available that are practical to use and hopefully general enough to productise. That said, a Bayesian toolbox can bring benefits by having a consistent framework to model unknown quantities, incorporate prior information, and, depending on the inference engine used, diagnose model fit to data.