The default behavior for adding and solving with noisemodels has changed from Pastas 1.5. Find more information here

Bayesian uncertainty analysis#

R.A. Collenteur, Eawag, June, 2023

In this notebook it is shown how the MCMC-algorithm can be used to estimate the model parameters and quantify the (parameter) uncertainties for a Pastas model using a Bayesian approach. For this the EmceeSolver is introduced, based on the emcee Python package.

Besides Pastas the following Python Packages have to be installed to run this notebook:

Note: The EmceeSolver is still an experimental feature and some of the arguments might be changed in the near future (2023/06/22). We welcome testing and feedback on this new feature!.
import numpy as np
import pandas as pd

import pastas as ps
import emcee
import corner

import matplotlib.pyplot as plt

Python version: 3.11.6
NumPy version: 1.26.4
Pandas version: 2.2.2
SciPy version: 1.13.0
Matplotlib version: 3.8.4
Numba version: 0.59.1
LMfit version: 1.3.1
Latexify version: Not Installed
Pastas version: 1.5.0

1. Create a Pastas Model#

The first step is to create a Pastas Model, including the RechargeModel to simulate the effect of precipitation and evaporation on the heads. Here, we first estimate the model parameters using the standard least-squares approach.

head = pd.read_csv(
    "data/B32C0639001.csv", parse_dates=["date"], index_col="date"

evap = pd.read_csv("data/evap_260.csv", index_col=0, parse_dates=[0]).squeeze()
rain = pd.read_csv("data/rain_260.csv", index_col=0, parse_dates=[0]).squeeze()

ml = ps.Model(head)

# Select a recharge model
rch = ps.rch.FlexModel()

rm = ps.RechargeModel(rain, evap, recharge=rch, rfunc=ps.Gamma(), name="rch")


ax = ml.plot(figsize=(10, 3))
Fit report head                   Fit Statistics
nfev    52                     EVP         89.38
nobs    351                    R2           0.89
noise   True                   RMSE         0.07
tmin    1990-01-01 00:00:00    AICc     -2060.83
tmax    2005-10-14 00:00:00    BIC      -2030.37
freq    D                      Obj          0.47
warmup  3650 days 00:00:00     ___              
solver  LeastSquares           Interp.        No

Parameters (8 optimized)
                optimal     initial   vary
rch_A          0.426129    0.630436   True
rch_n          0.670368    1.000000   True
rch_a        296.717504   10.000000   True
rch_srmax     53.419764  250.000000   True
rch_lp         0.250000    0.250000  False
rch_ks        19.840451  100.000000   True
rch_gamma      3.961175    2.000000   True
rch_kv         1.000000    1.000000  False
rch_simax      2.000000    2.000000  False
constant_d     0.805807    1.359779   True
noise_alpha   34.827372   15.000000   True

2. Use the EmceeSolver#

We will now use the EmceeSolve solver to estimate the model parameters and their uncertainties. This solver wraps the Emcee package, which implements different versions of MCMC. A good understanding of Emcee helps when using this solver, so it comes recommended to check out their documentation as well.

To set up the solver, a number of decisions need to be made:

  • Determine the priors of the parameters

  • Choose a (log) likelihood function

  • Choose the number of steps and thinning

2a. Choose and set the priors#

The first step is to choose and set the priors of the parameters. This is done by using the ml.set_parameter method and the dist argument (from distribution). Any distribution from the scipy.stats can be chosen (, for example uniform, norm, or lognorm. Here, for the sake of the example, we set all prior distributions to a normal distribution.

# Set the initial parameters to a normal distribution
for name in ml.parameters.index:
    ml.set_parameter(name, dist="norm")

initial pmin pmax vary name dist stderr optimal
rch_A 0.630436 0.00001 63.043598 True rch norm 0.025728 0.426129
rch_n 1.000000 0.01000 100.000000 True rch norm 0.020096 0.670368
rch_a 10.000000 0.01000 10000.000000 True rch norm 40.456274 296.717504
rch_srmax 250.000000 0.00001 1000.000000 True rch norm 3.038003 53.419764
rch_lp 0.250000 0.00001 1.000000 False rch norm NaN 0.250000
rch_ks 100.000000 0.00001 10000.000000 True rch norm 2.196407 19.840451
rch_gamma 2.000000 0.00001 20.000000 True rch norm 0.364272 3.961175
rch_kv 1.000000 0.25000 2.000000 False rch norm NaN 1.000000
rch_simax 2.000000 0.00000 10.000000 False rch norm NaN 2.000000
constant_d 1.359779 NaN NaN True constant norm 0.033039 0.805807
noise_alpha 15.000000 0.00001 5000.000000 True noise norm 4.109213 34.827372

Pastas will use the initial value of the parameter for the loc argument of the distribution (e.g., the mean of a normal distribution), and the stderr as the scale argument (e.g., the standard deviation of a normal distribution). Only for the parameters with a uniform distribution, the pmin and pmax values are used to determine a uniform prior. By default, all parameters are assigned a uniform prior.

Note: This means that either the `pmin` and `pmax` should be set for uniform distributions, or the `stderr` for any other distribution. That is why in this example model was first solved using LeastSquares, in order to obtain estimates for the `stderr`. In practice, these could also be set based on expert judgement or information about the parameters.

2b. Create the solver instance#

The next step is to create an instance of the EmceeSolve solver class. At this stage all the settings need to be provided on how the Ensemble Sampler is created ( Important settings are the nwalkers, the moves, the objective_function. More advanced options are to parallelize the MCMC algorithm (parallel=True), and to set a backend to store the results. Here’s an example:

# Choose the objective function
ln_prob = ps.objfunc.GaussianLikelihoodAr1()

# Create the EmceeSolver with some settings
s = ps.EmceeSolve(

In the above code we created an EmceeSolve instance with 20 walkers, which take steps according to the DEMove move algorithm (see Emcee docs), and a Gaussian likelihood function that assumes AR1 correlated errors. Different objective functions are available, see the Pastas documentation on the different options.

Depending on the likelihood function, a number of additional parameters need to be inferred. These parameters are not added to the Pastas Model instance, but are available from the solver object. Using the set_parameter method of the solver, these parameters can be changed. In this example where we use the GaussianLikelihoodAr1 function the sigma and theta are estimated; the unknown standard deviation of the errors and the autoregressive parameter.

initial pmin pmax vary stderr name dist
ln_sigma 0.05 1.000000e-10 1.00000 True 0.01 ln uniform
ln_theta 0.50 1.000000e-10 0.99999 True 0.20 ln uniform
s.set_parameter("ln_sigma", initial=0.0028, vary=False, dist="norm")
initial pmin pmax vary stderr name dist
ln_sigma 0.0028 1.000000e-10 1.00000 False 0.01 ln norm
ln_theta 0.5000 1.000000e-10 0.99999 True 0.20 ln uniform

2c. Run the solver and solve the model#

After setting the parameters and creating a EmceeSolve solver instance we are now ready to run the MCMC analysis. We can do this by running ml.solve. We can pass the same parameters that we normally provide to this method (e.g., tmin or fit_constant). Here we use the initial parameters from our least-square solve, and do not fit a noise model, because we take autocorrelated errors into account through the likelihood function.

All the arguments that are not used by ml.solve, for example steps and tune, are passed on to the run_mcmc method from the sampler (see Emcee docs). The most important is the steps argument, that determines how many steps each of the walkers takes.

# Use the solver to run MCMC
emcee: Exception while calling your likelihood function:
  params: [  0.44873518   0.67411594 308.63996982  56.16485699  22.7400996
   4.18393874   0.6057671 ]
  args: (False, None, None)
  kwargs: {}
 32%|███▏      | 318/1000 [00:29<01:03, 10.71it/s]Traceback (most recent call last):
  File "/home/docs/checkouts/", line 640, in __call__
    return self.f(x, *self.args, **self.kwargs)
  File "/home/docs/checkouts/", line 896, in log_probability
    return lp + self.log_likelihood(
  File "/home/docs/checkouts/", line 933, in log_likelihood
    rv = self.misfit(
  File "/home/docs/checkouts/", line 122, in misfit
    rv =
  File "/home/docs/checkouts/", line 521, in residuals
    sim = self.simulate(p, tmin, tmax, freq, warmup, return_warmup=False)
  File "/home/docs/checkouts/", line 464, in simulate
    if sim.hasnans:
  File "/home/docs/checkouts/", line 889, in hasnans
    return bool(isna(self).any())  # type: ignore[union-attr]
  File "/home/docs/checkouts/", line 6471, in any
    return self._reduce(
  File "/home/docs/checkouts/", line 6457, in _reduce
    return op(delegate, skipna=skipna, **kwds)
  File "/home/docs/checkouts/", line 482, in nanany
    def nanany(
 32%|███▏      | 319/1000 [00:29<01:03, 10.67it/s]
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[7], line 3
      1 # Use the solver to run MCMC
      2 ml.del_noisemodel()
----> 3 ml.solve(
      4     solver=s,
      5     initial=False,
      6     fit_constant=False,
      7     tmin="1990",
      8     steps=1000,
      9     tune=True,
     10 )

File ~/checkouts/, in Model.solve(self, tmin, tmax, freq, warmup, noise, solver, report, initial, weights, fit_constant, freq_obs, **kwargs)
    906 self.settings["solver"] = self.solver._name
    908 # Solve model
--> 909 success, optimal, stderr = self.solver.solve(
    910     noise=self.settings["noise"], weights=weights, **kwargs
    911 )
    912 if not success:
    913     logger.warning("Model parameters could not be estimated well.")

File ~/checkouts/, in EmceeSolve.solve(self, noise, weights, steps, callback, **kwargs)
    837 else:
    838     self.sampler = emcee.EnsembleSampler(
    839         nwalkers=self.nwalkers,
    840         ndim=ndim,
    845         args=(noise, weights, callback),
    846     )
--> 848     self.sampler.run_mcmc(pinit, steps, progress=self.progress_bar, **kwargs)
    850 # Get optimal values
    851 optimal = self.initial.copy()

File ~/checkouts/, in EnsembleSampler.run_mcmc(self, initial_state, nsteps, **kwargs)
    447     initial_state = self._previous_state
    449 results = None
--> 450 for results in self.sample(initial_state, iterations=nsteps, **kwargs):
    451     pass
    453 # Store so that the ``initial_state=None`` case will work

File ~/checkouts/, in EnsembleSampler.sample(self, initial_state, log_prob0, rstate0, blobs0, iterations, tune, skip_initial_state_check, thin_by, thin, store, progress, progress_kwargs)
    406 move = self._random.choice(self._moves, p=self._weights)
    408 # Propose
--> 409 state, accepted = move.propose(model, state)
    410 state.random_state = self.random_state
    412 if tune:

File ~/checkouts/, in RedBlueMove.propose(self, model, state)
     90 q, factors = self.get_proposal(s, c, model.random)
     92 # Compute the lnprobs of the proposed position.
---> 93 new_log_probs, new_blobs = model.compute_log_prob_fn(q)
     95 # Loop over the walkers and update them accordingly.
     96 for i, (j, f, nlp) in enumerate(
     97     zip(all_inds[S1], factors, new_log_probs)
     98 ):

File ~/checkouts/, in EnsembleSampler.compute_log_prob(self, coords)
    494     else:
    495         map_func = map
--> 496     results = list(map_func(self.log_prob_fn, p))
    498 try:
    499     # perhaps log_prob_fn returns blobs?
    504     # l is a length-1 array, np.array([1.234]). In that case blob
    505     # will become an empty list.
    506     blob = [l[1:] for l in results if len(l) > 1]

File ~/checkouts/, in _FunctionWrapper.__call__(self, x)
    638 def __call__(self, x):
    639     try:
--> 640         return self.f(x, *self.args, **self.kwargs)
    641     except:  # pragma: no cover
    642         import traceback

File ~/checkouts/, in EmceeSolve.log_probability(self, p, noise, weights, callback)
    894     return -np.inf
    895 else:
--> 896     return lp + self.log_likelihood(
    897         p, noise=noise, weights=weights, callback=callback
    898     )

File ~/checkouts/, in EmceeSolve.log_likelihood(self, p, noise, weights, callback)
    930 # Set the parameters that are varied from the model and objective function
    931 par[self.vary] = p
--> 933 rv = self.misfit(
    934     p=par[: -self.objective_function.nparam],
    935     noise=noise,
    936     weights=weights,
    937     callback=callback,
    938 )
    940 lnlike = self.objective_function.compute(
    941     rv, par[-self.objective_function.nparam :]
    942 )
    944 return lnlike

File ~/checkouts/, in BaseSolver.misfit(self, p, noise, weights, callback, returnseparate)
    119     rv = *
    121 else:
--> 122     rv =
    124 # Determine if weights need to be applied
    125 if weights is not None:

File ~/checkouts/, in Model.residuals(self, p, tmin, tmax, freq, warmup)
    518     freq_obs = self.settings["freq_obs"]
    520 # simulate model
--> 521 sim = self.simulate(p, tmin, tmax, freq, warmup, return_warmup=False)
    523 # Get the oseries calibration series
    524 oseries_calib = self.observations(tmin, tmax, freq_obs)

File ~/checkouts/, in Model.simulate(self, p, tmin, tmax, freq, warmup, return_warmup)
    461 if not return_warmup:
    462     sim = sim.loc[tmin:tmax]
--> 464 if sim.hasnans:
    465     msg = (
    466         "Simulation contains NaN-values. Check if time series settings "
    467         "are provided for each stress model "
    468         "(e.g. `ps.StressModel(stress, settings='prec')`!"
    469     )
    470     logger.error(msg)

File ~/checkouts/, in IndexOpsMixin.hasnans(self)
    866 """
    867 Return True if there are any NaNs.
    885 True
    886 """
    887 # error: Item "bool" of "Union[bool, ndarray[Any, dtype[bool_]], NDFrame]"
    888 # has no attribute "any"
--> 889 return bool(isna(self).any())

File ~/checkouts/, in Series.any(self, axis, bool_only, skipna, **kwargs)
   6469 nv.validate_logical_func((), kwargs, fname="any")
   6470 validate_bool_kwarg(skipna, "skipna", none_allowed=False)
-> 6471 return self._reduce(
   6472     nanops.nanany,
   6473     name="any",
   6474     axis=axis,
   6475     numeric_only=bool_only,
   6476     skipna=skipna,
   6477     filter_type="bool",
   6478 )

File ~/checkouts/, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   6452     # GH#47500 - change to TypeError to match other methods
   6453     raise TypeError(
   6454         f"Series.{name} does not allow {kwd_name}={numeric_only} "
   6455         "with non-numeric dtypes."
   6456     )
-> 6457 return op(delegate, skipna=skipna, **kwds)

File ~/checkouts/, in nanany(values, axis, skipna, mask)
    477         return func(values, axis=axis, **kwargs)
    479     return cast(F, newfunc)
--> 482 def nanany(
    483     values: np.ndarray,
    484     *,
    485     axis: AxisInt | None = None,
    486     skipna: bool = True,
    487     mask: npt.NDArray[np.bool_] | None = None,
    488 ) -> bool:
    489     """
    490     Check if any elements along an axis evaluate to True.
    514     False
    515     """
    516     if values.dtype.kind in "iub" and mask is None:
    517         # GH#26032 fastpath
    518         # error: Incompatible return value type (got "Union[bool_, ndarray]",
    519         # expected "bool")


3. Posterior parameter distributions#

The results from the MCMC analysis are stored in the sampler object, accessible through ml.solver.sampler variable. The object ml.solver.sampler.flatchain contains a Pandas DataFrame with \(n\) the parameter samples, where \(n\) is calculated as follows:

\(n = \frac{\left(\text{steps}-\text{burn}\right)\cdot\text{nwalkers}}{\text{thin}} \)

Corner is a simple but great python package that makes creating corner graphs easy. A couple of lines of code suffice to create a plot of the parameter distributions and the covariances between the parameters.

# Corner plot of the results
fig = plt.figure(figsize=(8, 8))

labels = list(ml.parameters.index[ml.parameters.vary]) + list(
labels = [label.split("_")[1] for label in labels]

best = list(ml.parameters[ml.parameters.vary == True].optimal) + list(
    ml.solver.parameters[ml.solver.parameters.vary == True].optimal

axes = corner.corner(
    ml.solver.sampler.get_chain(flat=True, discard=500),
    quantiles=[0.025, 0.5, 0.975],

4. What happens to the walkers at each step?#

The walkers take steps in different directions for each step. It is expected that after a number of steps, the direction of the step becomes random, as a sign that an optimum has been found. This can be checked by looking at the autocorrelation, which should be insignificant after a number of steps. Below we just show how to obtain the different chains, the interpretation of which is outside the scope of this notebook.

fig, axes = plt.subplots(len(labels), figsize=(10, 7), sharex=True)

samples = ml.solver.sampler.get_chain(flat=True)
for i in range(len(labels)):
    ax = axes[i]
    ax.plot(samples[:, i], "k", alpha=0.5)
    ax.set_xlim(0, len(samples))
    ax.yaxis.set_label_coords(-0.1, 0.5)

axes[-1].set_xlabel("step number")

5. Plot some simulated time series to display uncertainty?#

We can now draw parameter sets from the chain and simulate the uncertainty in the head simulation.

# Plot results and uncertainty
ax = ml.plot(figsize=(10, 3))

chain = ml.solver.sampler.get_chain(flat=True, discard=500)
inds = np.random.randint(len(chain), size=100)
for ind in inds:
    params = chain[ind]
    p = ml.parameters.optimal.copy().values
    p[ml.parameters.vary] = params[: ml.parameters.vary.sum()]
    l = ml.simulate(p, tmin="1990").plot(c="gray", alpha=0.1, zorder=-1)

plt.legend(["Measurements", "Simulation", "Ensemble members"], numpoints=3)