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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
Andreas Griewank,Andrea Walther +1 more
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The article was published on 2008-01-01. It has received 972 citations till now. The article focuses on the topics: Automatic differentiation.read more
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brms: An R Package for Bayesian Multilevel Models Using Stan
TL;DR: The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan, allowing users to fit linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multileVEL context.
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CasADi: a software framework for nonlinear optimization and optimal control
TL;DR: This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.
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The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D. Homan,Andrew Gelman +1 more
TL;DR: The No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L, and derives a method for adapting the step size parameter {\epsilon} on the fly based on primal-dual averaging.
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Automatic differentiation in machine learning: a survey
TL;DR: Automatic differentiation (AD) is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs as discussed by the authors, which is a small but established field with applications in areas including computational uid dynamics, atmospheric sciences, and engineering design optimization.
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Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization.
TL;DR: Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia.
Related Papers (5)
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
Andreas Griewank,Andrea Walther +1 more