• Spearmint

  • Referenced in 60 articles [sw17859]
  • software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus...
  • Stan

  • Referenced in 174 articles [sw10200]
  • Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with Optimization...
  • Auto-WEKA

  • Referenced in 23 articles [sw21536]
  • WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. WEKA is a widely used ... using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated...
  • acebayes

  • Referenced in 11 articles [sw20243]
  • acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange ... package acebayes to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with ... design maximising an expected utility. Finding Bayesian optimal designs for realistic problems is challenging...
  • ToulBar2

  • Referenced in 22 articles [sw07289]
  • Weighted Max-SAT, Quadratic Pseudo-Boolean Optimization, and Bayesian Networks...
  • HumanEva

  • Referenced in 23 articles [sw15489]
  • that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance ... view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets...
  • PESC

  • Referenced in 6 articles [sw17860]
  • general framework for constrained Bayesian optimization using information-based search. We present an information-theoretic ... framework for solving global black-box optimization problems that also have black-box constraints ... towards a unified solution for constrained Bayesian optimization...
  • BayesOpt

  • Referenced in 5 articles [sw12003]
  • BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits. BayesOpt ... library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits ... sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds...
  • RStan

  • Referenced in 50 articles [sw13990]
  • rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
  • BOCK

  • Referenced in 3 articles [sw32130]
  • BOCK : Bayesian Optimization with Cylindrical Kernels. A major challenge in Bayesian Optimization is the boundary ... this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea...
  • GOBNILP

  • Referenced in 5 articles [sw29883]
  • GOBNILP (Globally Optimal Bayesian Network learning using Integer Linear Programming) is a C program which...
  • designv2

  • Referenced in 3 articles [sw27807]
  • involving log(potency) in comparative binary bioassays. Optimal designs are investigated for bioassays involving ... main interest. Local and Bayesian D-optimal designs are considered, as well ... prior distributions used for the Bayesian optimal designs includes uniform, trivariate normal and a bivariate ... lack of closed form solutions for Bayesian optimal designs, much of the investigation is numerical...
  • mlrMBO

  • Referenced in 4 articles [sw19214]
  • based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black...
  • PARyOpt

  • Referenced in 2 articles [sw25894]
  • software for Parallel Asynchronous Remote Bayesian Optimization. PARyOpt is a python based implementation ... Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive ... next campaign of function calls. Bayesian optimization provides an elegant approach to overcome this issue ... implement a parallel, asynchronous variant of Bayesian optimization. The framework is robust and resilient...
  • DoseFinding

  • Referenced in 10 articles [sw08194]
  • models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation...
  • GPflowOpt

  • Referenced in 2 articles [sw33396]
  • GPflowOpt: A Bayesian Optimization Library using TensorFlow. A novel Python framework for Bayesian optimization known ... differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that ... value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy...
  • BUQO

  • Referenced in 4 articles [sw34653]
  • imaging inverse problems via convex optimization. We propose a Bayesian uncertainty quantification method for large ... inverse problems. Our method applies to all Bayesian models that are log-concave, where maximum ... posteriori (MAP) estimation is a convex optimization problem. The method is a framework to analyze ... illustrate our methodology, dubbed BUQO (Bayesian Uncertainty Quantification by Optimization), on a range of challenging...
  • tgp

  • Referenced in 35 articles [sw07921]
  • model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary ... expected improvement. The latter supports derivative-free optimization of noisy black-box functions...
  • SafeOpt

  • Referenced in 2 articles [sw35418]
  • SafeOpt: Safe Bayesian Optimization. This code implements an adapted version of the safe, Bayesian optimization...
  • ELFI

  • Referenced in 3 articles [sw26681]
  • central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which...