Stochastic Toy Problems

The stochastic module provides toy models, distributions and error measures that can be used for tests and in examples.

• Markov Jump Model
  • MarkovJumpModel
    • MarkovJumpModel.interpolate_mol_counts()
    • MarkovJumpModel.n_outputs()
    • MarkovJumpModel.n_parameters()
    • MarkovJumpModel.simulate()
    • MarkovJumpModel.simulate_raw()
    • MarkovJumpModel.suggested_parameters()
    • MarkovJumpModel.suggested_times()
• Stochastic degradation model
  • DegradationModel
    • DegradationModel.interpolate_mol_counts()
    • DegradationModel.mean()
    • DegradationModel.n_outputs()
    • DegradationModel.n_parameters()
    • DegradationModel.simulate()
    • DegradationModel.simulate_raw()
    • DegradationModel.suggested_parameters()
    • DegradationModel.suggested_times()
    • DegradationModel.variance()
• Stochastic Logistic Model
  • LogisticModel
    • LogisticModel.interpolate_mol_counts()
    • LogisticModel.mean()
    • LogisticModel.n_outputs()
    • LogisticModel.n_parameters()
    • LogisticModel.simulate()
    • LogisticModel.simulate_raw()
    • LogisticModel.suggested_parameters()
    • LogisticModel.suggested_times()
• Stochastic Michaelis Menten model
  • MichaelisMentenModel
    • MichaelisMentenModel.interpolate_mol_counts()
    • MichaelisMentenModel.n_outputs()
    • MichaelisMentenModel.n_parameters()
    • MichaelisMentenModel.simulate()
    • MichaelisMentenModel.simulate_raw()
    • MichaelisMentenModel.suggested_parameters()
    • MichaelisMentenModel.suggested_times()
• Stochastic production and degradation model
  • ProductionDegradationModel
    • ProductionDegradationModel.interpolate_mol_counts()
    • ProductionDegradationModel.n_outputs()
    • ProductionDegradationModel.n_parameters()
    • ProductionDegradationModel.simulate()
    • ProductionDegradationModel.simulate_raw()
    • ProductionDegradationModel.suggested_parameters()
    • ProductionDegradationModel.suggested_times()
• Schlogl’s model
  • SchloglModel
    • SchloglModel.interpolate_mol_counts()
    • SchloglModel.n_outputs()
    • SchloglModel.n_parameters()
    • SchloglModel.simulate()
    • SchloglModel.simulate_raw()
    • SchloglModel.suggested_parameters()
    • SchloglModel.suggested_times()