Gallery¶
Here we provide a gallery containing selected example results of several applications (a non-exhaustive list) using SPUX framework for Bayesian inference and uncertainty quantification.
Randomwalk¶
A simple one-dimensional randomwalk with uncertain origin, drift, and the observation error.
Linear bucket¶
rpy2
bindings to Python.Work in progress.
Stochastic inputs¶
numba
compiled C code for computationally expensive parts.Publication: Del Giudice, D. et al., (2016) “Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation; Water Resources Research. John Wiley & Sons, Ltd, 52(4), pp. 3162–3186. doi: 10.1002/2015WR017871.
Work in progress.
Stochastic parameters¶
ctypes
bindings of the compiled Fortran model library to Python.Work in progress.
Prey-Predator¶
JPype
bindings to Python.Publication (preprint available at http://arxiv.org/abs/1711.01410):
Šukys, J. and Kattwinkel, M.
"SPUX: Scalable Particle Markov Chain Monte Carlo
for uncertainty quantification in stochastic ecological models".
Advances in Parallel Computing - Parallel Computing is Everywhere,
IOS Press, (32), pp. 159–168, 2018.
Work in progress.
River invertebrates¶
JPype
bindings to Python.Work in progress.
DATALAKES¶
ctypes
bindings of the compiled Fortran model library to Python.Work in progress.
In-stream herbicides¶
ctypes
bindings of the compiled Fortran model library to Python.Work in progress.
Urban hydrology¶
Swig
wrapper for Python.Work in progress.
Solar dynamo¶
BISTOM - calibration of the solar dynamo simulations.
Work in progress.