This Jupyter notebook can be downloaded from bayesian-example-NGC6440E.ipynb, or viewed as a python script at bayesian-example-NGC6440E.py.

# PINT Bayesian Interface Examples

```
[1]:
```

```
from pint.models import get_model, get_model_and_toas
from pint.bayesian import BayesianTiming
from pint.config import examplefile
from pint.models.priors import Prior
from pint.logging import setup as setup_log
from scipy.stats import uniform
```

```
[2]:
```

```
import numpy as np
import emcee
import nestle
import corner
import io
import matplotlib.pyplot as plt
```

```
[3]:
```

```
# Turn off log messages. They can slow down the processing.
setup_log(level="WARNING")
```

```
[3]:
```

```
1
```

```
[4]:
```

```
# Read the par and tim files
parfile = examplefile("NGC6440E.par.good")
timfile = examplefile("NGC6440E.tim")
model, toas = get_model_and_toas(parfile, timfile)
```

```
[5]:
```

```
# This is optional, but the likelihood function behaves better if
# we have the pulse numbers. Make sure that your timing solution is
# phase connected before doing this.
toas.compute_pulse_numbers(model)
```

```
[6]:
```

```
# Now set the priors.
# I am cheating here by setting the priors around the maximum likelihood estimates.
# This is a bad idea for real datasets and can bias the estimates. I am doing this
# here just to make everything finish faster. In the real world, these priors should
# be informed by, e.g. previous (independent) timing solutions, pulsar search results,
# VLBI localization etc. Note that unbounded uniform priors don't work here.
for par in model.free_params:
param = getattr(model, par)
param_min = float(param.value - 10 * param.uncertainty_value)
param_span = float(20 * param.uncertainty_value)
param.prior = Prior(uniform(param_min, param_span))
```

```
[7]:
```

```
# Now let us create a BayesianTiming object. This is a wrapper around the
# PINT API that provides provides lnlikelihood, lnprior and prior_transform
# functions which can be passed to a sampler of your choice.
bt = BayesianTiming(model, toas, use_pulse_numbers=True)
```

```
[8]:
```

```
print("Number of parameters = ", bt.nparams)
print("Likelihood method = ", bt.likelihood_method)
```

```
Number of parameters = 5
Likelihood method = wls
```

## MCMC sampling using emcee

```
[9]:
```

```
nwalkers = 20
sampler = emcee.EnsembleSampler(nwalkers, bt.nparams, bt.lnposterior)
```

```
[10]:
```

```
# Choose the MCMC start points in the vicinity of the maximum likelihood estimate
# available in the `model` object. This helps the MCMC chains converge faster.
# We can also draw these points from the prior, but the MCMC chains will converge
# slower in that case.
maxlike_params = np.array([param.value for param in bt.params], dtype=float)
maxlike_errors = [param.uncertainty_value for param in bt.params]
start_points = (
np.repeat([maxlike_params], nwalkers).reshape(bt.nparams, nwalkers).T
+ np.random.randn(nwalkers, bt.nparams) * maxlike_errors
)
```

```
[11]:
```

```
# ** IMPORTANT!!! **
# This is used to exclude some of the following time-consuming steps from the readthedocs build.
# Set this to False while actually using this example.
rtd = True
```

```
[12]:
```

```
# Use longer chain_length for real runs. It is kept small here so that
# the sampling finishes quickly (and because I know the burn in is short
# because of the cheating priors above).
if not rtd:
print("Running emcee...")
chain_length = 1000
sampler.run_mcmc(
start_points,
chain_length,
progress=True,
)
```

```
[13]:
```

```
if not rtd:
# Merge all the chains together after discarding the first 100 samples as 'burn-in'.
# The burn-in should be decided after looking at the chains in the real world.
samples_emcee = sampler.get_chain(flat=True, discard=100)
# Plot the MCMC chains to make sure that the burn-in has been removed properly.
# Otherwise, go back and discard more points.
for idx, param_chain in enumerate(samples_emcee.T):
plt.subplot(bt.nparams, 1, idx + 1)
plt.plot(param_chain, label=bt.param_labels[idx])
plt.legend()
plt.show()
```

```
[14]:
```

```
# Plot the posterior distribution.
if not rtd:
fig = corner.corner(samples_emcee, labels=bt.param_labels)
plt.show()
```

## Nested sampling with nestle

Nested sampling computes the Bayesian evidence along with posterior samples. This allows us to do compare two models. Let us compare the model above with and without an EFAC.

```
[15]:
```

```
# Let us run the model without EFAC first. We can reuse the `bt` object from before.
# Nesle is really simple :)
# method='multi' runs the MultiNest algorithm.
# `npoints` is the number of live points.
# `dlogz` is the target accuracy in the computed Bayesian evidence.
# Increasing `npoints` or decreasing `dlogz` gives more accurate results,
# but at the cost of time.
if not rtd:
print("Running nestle...")
result_nestle_1 = nestle.sample(
bt.lnlikelihood,
bt.prior_transform,
bt.nparams,
method="multi",
npoints=150,
dlogz=0.5,
callback=nestle.print_progress,
)
```

```
[16]:
```

```
# Plot the posterior
# The nested samples come with weights, which must be taken into account
# while plotting.
if not rtd:
fig = corner.corner(
result_nestle_1.samples,
weights=result_nestle_1.weights,
labels=bt.param_labels,
range=[0.999] * bt.nparams,
)
plt.show()
```

Let us create a new model with an EFAC applied to all toas (all TOAs in this dataset are from GBT).

```
[17]:
```

```
# casting the model to str gives the par file representation.
# Add an EFAC to the par file and make it unfrozen.
parfile = f"{str(model)}EFAC TEL gbt 1 1"
model2 = get_model(io.StringIO(parfile))
```

```
[18]:
```

```
# Now set the priors.
# Again, don't do this with real data. Use uninformative priors or priors
# motivated by previous experiments. This is done here with the sole purpose
# of making the run finish fast. Let us try this with the prior_info option now.
prior_info = {}
for par in model2.free_params:
param = getattr(model2, par)
param_min = float(param.value - 10 * param.uncertainty_value)
param_max = float(param.value + 10 * param.uncertainty_value)
prior_info[par] = {"distr": "uniform", "pmin": param_min, "pmax": param_max}
prior_info["EFAC1"] = {"distr": "normal", "mu": 1, "sigma": 0.1}
```

```
[19]:
```

```
bt2 = BayesianTiming(model2, toas, use_pulse_numbers=True, prior_info=prior_info)
print(bt2.likelihood_method)
```

```
wls
```

```
[20]:
```

```
if not rtd:
result_nestle_2 = nestle.sample(
bt2.lnlikelihood,
bt2.prior_transform,
bt2.nparams,
method="multi",
npoints=150,
dlogz=0.5,
callback=nestle.print_progress,
)
```

```
[21]:
```

```
# Plot the posterior.
# The EFAC looks consistent with 1.
if not rtd:
fig2 = corner.corner(
result_nestle_2.samples,
weights=result_nestle_2.weights,
labels=bt2.param_labels,
range=[0.999] * bt2.nparams,
)
plt.show()
```

Now let us look at the evidences and compute the Bayes factor.

```
[22]:
```

```
if not rtd:
print(
f"Evidence without EFAC : {result_nestle_1.logz} +/- {result_nestle_1.logzerr}"
)
print(f"Evidence with EFAC : {result_nestle_2.logz} +/- {result_nestle_2.logzerr}")
bf = np.exp(result_nestle_1.logz - result_nestle_2.logz)
print(f"Bayes factor : {bf} (in favor of no EFAC)")
```

The Bayes factor tells us that the EFAC is unnecessary for this dataset.