This page contains a number of examples showing how to use Pints.
Each example was created as a Jupyter notebook (http://jupyter.org/). These notebooks can be downloaded and used, or you can simply copy/paste the relevant code.
- Optimisation: First example
- Sampling: First example
- Writing a model
- Writing a custom LogPDF
- Writing a custom LogPrior
- Full control with the ask-and-tell interface
- Optimisation in a transformed parameter space - using a Transform object
- Optimisation in a transformed parameter space - writing a model wrapper
- Sampling in a transformed parameter space - using a Transform object
- Sampling in a transformed parameter space - with or without Jacobian adjustment
- Convenience methods fmin() and curve_fit()
- Maximum loglikelihood
- Multiple objectives
- Spotting unidentifiable parameters with MCMC
- Visualising a 2d error surface
- Differential Evolution MCMC
- DRAM ACMC
- DREAM MCMC
- Emcee Hammer
- Haario Adaptive Covariance MCMC
- Haario-Bardenet Adaptive Covariance MCMC
- Metropolis Random Walk MCMC
- Population MCMC
- Rao-Blackwell Adaptive Covariance MCMC
- Slice Sampling: Doubling MCMC
- Slice Sampling: Overrelaxation MCMC
- Slice Sampling: Stepout MCMC
- Hamiltonian MCMC
- MALA MCMC
- Monomial-Gamma HMC MCMC
- No-U-Turn MCMC
- Relativistic MCMC
- Slice Sampling: Rank Shrinking MCMC
- Autocorrelation
- Customise analysis plots
- Effective sample size
- Evaluating noise models using autocorrelation plots of the residuals
- Histogram plots
- Noise model diagnostic plots (correlation)
- Noise model diagnostic plots (magnitude)
- Pairwise scatterplots
- Predicted time series
- Trace plots
- Autoregressive moving average errors
- Cauchy sampling error
- Constant and multiplicative Gaussian error
- Integrated noise model
- Log priors
- Multiplicative Gaussian noise
- Pooling parameters
- Student-t noise model
- Beeler-Reuter action potential model
- Constant model
- Fitzhugh-Nagumo model
- Goodwin oscillator model
- HES1 Michaelis-Menten model
- Hodgkin-Huxley Potassium current model
- Logistic growth model
- Lotka-Volterra predator-prey model
- Repressilator model
- Simple Harmonic Oscillator model
- SIR Epidemiology model
- Stochastic Degradation model
- Stochastic Logistic model