diff --git a/Paper/Examples/fully_coherent_search_using_MCMC_convergence.py b/Paper/Examples/fully_coherent_search_using_MCMC_convergence.py
new file mode 100644
index 0000000000000000000000000000000000000000..e440eb37dae67e156b02c08f16247a76cd7aebca
--- /dev/null
+++ b/Paper/Examples/fully_coherent_search_using_MCMC_convergence.py
@@ -0,0 +1,64 @@
+import pyfstat
+import numpy as np
+
+# Properties of the GW data
+sqrtSX = 1e-23
+tstart = 1000000000
+duration = 100*86400
+tend = tstart + duration
+
+# Properties of the signal
+F0 = 30.0
+F1 = -1e-10
+F2 = 0
+Alpha = 5e-3
+Delta = 6e-2
+tref = .5*(tstart+tend)
+
+depth = 10
+h0 = sqrtSX / depth
+data_label = 'fully_coherent_search_using_MCMC_convergence'
+
+data = pyfstat.Writer(
+    label=data_label, outdir='data', tref=tref,
+    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
+    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
+data.make_data()
+
+# The predicted twoF, given by lalapps_predictFstat can be accessed by
+twoF = data.predict_fstat()
+print 'Predicted twoF value: {}\n'.format(twoF)
+
+DeltaF0 = 5e-7
+DeltaF1 = 1e-12
+VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
+VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
+print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
+
+theta_prior = {'F0': {'type': 'unif',
+                      'lower': F0-DeltaF0/2.,
+                      'upper': F0+DeltaF0/2.},
+               'F1': {'type': 'unif',
+                      'lower': F1-DeltaF1/2.,
+                      'upper': F1+DeltaF1/2.},
+               'F2': F2,
+               'Alpha': Alpha,
+               'Delta': Delta
+               }
+
+ntemps = 1
+log10temperature_min = -1
+nwalkers = 100
+nsteps = [900, 100]
+
+mcmc = pyfstat.MCMCSearch(
+    label='fully_coherent_search_using_MCMC_convergence', outdir='data',
+    sftfilepath='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
+    minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
+    ntemps=ntemps, log10temperature_min=log10temperature_min)
+mcmc.setup_convergence_testing(
+    convergence_threshold_number=5, convergence_plot_upper_lim=10,
+    convergence_burnin_fraction=0.1)
+mcmc.run(context='paper', subtractions=[30, -1e-10])
+mcmc.plot_corner(add_prior=True)
+mcmc.print_summary()