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    SciMLSensitivity.jl logo
    SciMLSensitivity.jl
    • SciMLSensitivity.jl: Automatic Differentiation and Adjoints for (Differential) Equation Solvers
    • Tutorials
        • Differentiating an ODE Solution with Automatic Differentiation
        • Direct Sensitivity Analysis Functionality
        • Adjoint Sensitivity Analysis of Continuous Functionals
        • Sensitivity analysis for chaotic systems (shadowing methods)
        • Optimization of Ordinary Differential Equations
        • Parameter Estimation on Highly Stiff Systems
        • Handling Exogenous Input Signals
        • Data-Parallel Multithreaded, Distributed, and Multi-GPU Batching
        • Prediction error method (PEM)
        • Newton and Hessian-Free Newton-Krylov with Second Order Adjoint Sensitivity Analysis
        • Neural Second Order Ordinary Differential Equation
        • Strategies to Avoid Local Minima
        • Handling Divergent and Unstable Trajectories
        • Simultaneous Fitting of Multiple Neural Networks
        • Neural Ordinary Differential Equations with Flux
        • Neural Graph Differential Equations
        • Training a Neural Ordinary Differential Equation with Mini-Batching
        • Optimization of Stochastic Differential Equations
        • Delay Differential Equations
        • Enforcing Physical Constraints via Universal Differential-Algebraic Equations
        • Partial Differential Equation (PDE) Constrained Optimization
        • Training Neural Networks in Hybrid Differential Equations
        • Bouncing Ball Hybrid ODE Optimization
        • Bayesian Estimation of Differential Equations with Probabilistic Programming
        • Solving Optimal Control Problems with Universal Differential Equations
        • Universal Differential Equations for Neural Feedback Control
        • Controlling Stochastic Differential Equations
    • Manual and APIs
      • Sensitivity Algorithms for Differential Equations with Automatic Differentiation (AD)
      • Sensitivity Algorithms for Nonlinear Problems with Automatic Differentiation (AD)
      • Direct Forward Sensitivity Analysis of ODEs
      • Direct Adjoint Sensitivities of Differential Equations
    • Benchmarks
    • Sensitivity Math Details
    Version
    • Tutorials
    • Bayesian Estimation Tutorials
    • Bayesian Estimation of Differential Equations with Probabilistic Programming
    • Bayesian Estimation of Differential Equations with Probabilistic Programming
    Edit on GitHub

    Bayesian Estimation of Differential Equations with Probabilistic Programming

    For a good overview of how to use the tools of SciML in conjunction with the Turing.jl probabilistic programming language, see the Bayesian Differential Equation Tutorial.

    « Bouncing Ball Hybrid ODE OptimizationSolving Optimal Control Problems with Universal Differential Equations »

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