Stochastic state-space Inference and Prediction

pySIP documentation

• Quickstart
  • Installation
  • Usage
    • Data preparation
    • Parameters configuration and model initialization
    • Model evaluation
    • Simple (frequentist) parameter estimation
    • Bayesian parameter estimation
• PySIP advanced usage and internals
  • Parameters and Priors
    • Constrained and unconstrained parameter space
    • Transform and scale
    • Prior
    • Parameters collection and fixed parameters
  • Statespaces
  • Statespace estimator
  • Regressor
• Contributor Guidelines
  • License
  • Code of Conduct
• Cookbook: pySIP by example
  • Gausian Process Regression with Covariance Product and Sum
    • Load and prepare the Mauna Loa CO2 data set
    • Initialize parameters and models
    • Initialize the regressor
    • Do the frequentist fit
  • Matern regression model
  • Periodic regression model
  • Armadillo real world example - Bayesian fit
  • Armadillo real world example - Frequentist fit

Reference

• PySIP API
  • Regressor
    • Regressor
      • Regressor.estimate_output()
      • Regressor.estimate_states()
      • Regressor.eval_residuals()
      • Regressor.fit()
      • Regressor.log_likelihood()
      • Regressor.log_posterior()
      • Regressor.posterior_predictive()
      • Regressor.predict()
      • Regressor.prepare_data()
      • Regressor.prior_predictive()
      • Regressor.sample()
      • Regressor.simulate()
  • Statespace Estimators
    • KalmanQR
      • KalmanQR.estimate_output()
      • KalmanQR.filtering()
      • KalmanQR.log_likelihood()
      • KalmanQR.predict()
      • KalmanQR.simulate()
      • KalmanQR.smoothing()
      • KalmanQR.update()
  • Parameters and Priors
    • Parameters
      • Parameter
      • Parameters
    • Transforms
      • FixedTransform
      • LogTransform
      • LogitTransform
      • LowerTransform
      • NoneTransform
      • UpperTransform
      • auto_transform()
    • Priors
      • BasePrior
      • Beta
      • Gamma
      • InverseGamma
      • LogNormal
      • Normal
      • Uniform
  • Statespaces
    • StateSpace
      • StateSpace.discretization()
      • StateSpace.get_discrete_ssm()
      • StateSpace.prepare_data()
      • StateSpace.set_constant_continuous_ssm()
      • StateSpace.update()
      • StateSpace.update_continuous_ssm()
    • RCModel
      • RCModel.discretization()
    • GPModel
      • GPModel.discretization()
• List of implemented models
  • Thermal networks
    • R2C2Qgh
    • TiTh_RwRhAi
    • TiTm_RwRmAi
    • Ti_RA
    • Ti_RAcv
    • TwTiTb_RoRiRbAwAicv
    • TwTiTb_RoRiRibRbbAwAicv
    • TwTiTh_RoRiRhAwAi
    • TwTiTh_RoRiRhAwAicv
    • TwTiTh_RoRiRhAwAicv_bis
    • TwTiTm_RoRiAwAi
    • TwTiTm_RoRiAwAicv
    • TwTiTm_RoRiRbAwAicv
    • TwTiTm_RoRiRmAwAi
    • TwTiTm_RoRiRmAwAiAm
    • TwTiTm_RoRiRmAwAicv
    • TwTiTm_RoRiRmRbAwAi
    • TwTiTm_RoRiRmRbAwAicv
    • TwTi_RoRi
    • TwTi_RoRiAwAi
    • TwTi_RoRiAwAicv
    • TwTi_RoRiRbAwAi
    • TwTi_RoRiRbAwAicv
  • Gaussian processes
    • GPProduct
    • GPSum
    • Matern12
    • Matern32
    • Matern52
    • Periodic
    • QuasiPeriodic12
    • QuasiPeriodic32

Indices and tables

• Index

• Module Index

• Search Page