# SMC samplers¶

SMC samplers are SMC algorithms that sample from a sequence of target distributions. In this tutorial, these target distributions will be Bayesian posterior distributions of static models. SMC samplers are covered in Chapter 17 of the book.

## Defining a static model¶

A static model is a Python object that represents a Bayesian model with static parameter $$\theta$$. One may define a static model by subclassing base class StaticModel, and defining method logpyt, which evaluates the log-likelihood of datapoint $$Y_t$$ (given $$\theta$$ and past datapoints $$Y_{0:t-1}$$). Here is a simple example:

[1]:

%matplotlib inline
import warnings; warnings.simplefilter('ignore')  # hide warnings

from matplotlib import pyplot as plt
import seaborn as sb
from scipy import stats

import particles
from particles import smc_samplers as ssp
from particles import distributions as dists

class ToyModel(ssp.StaticModel):
def logpyt(self, theta, t):  # density of Y_t given theta and Y_{0:t-1}
return stats.norm.logpdf(self.data[t], loc=theta['mu'],
scale = theta['sigma'])


In words, we are considering a model where the observations are $$Y_t\sim N(\mu, \sigma^2)$$. The parameter is $$\theta=(\mu, \sigma)$$.

Class ToyModel contains information about the likelihood of the considered model, but not about its prior, or the considered data. First, let’s define those:

[2]:

T = 1000
my_data = stats.norm.rvs(loc=3.14, size=T)  # simulated data
my_prior = dists.StructDist({'mu': dists.Normal(scale=10.),
'sigma': dists.Gamma()})


For more details about to define prior distributions, see the documentation of module distributions, or the previous tutorial on Bayesian estimation of state-space models. Now that we have everything, let’s specify our static model:

[3]:

my_static_model = ToyModel(data=my_data, prior=my_prior)


This time, object my_static_model has enough information to define the posterior distribution(s) of the model (given all data, or part of the data). In fact, it inherits from StaticModel method logpost, which evaluates (for a collection of $$\theta$$ values) the posterior log-density at any time $$t$$ (meaning given data $$y_{0:t}$$).

[4]:

thetas = my_prior.rvs(size=5)
my_static_model.logpost(thetas, t=2)  # if t is omitted, gives the full posterior

[4]:

array([ -103.81939366,    -8.74342665, -1574.47064329,   -33.24706434,
-89.17306505])


The input of logpost (and output of myprior.rvs()) is a structured array, with the same keys as the prior distribution:

[5]:

thetas['mu'][0]

[5]:

4.5636339239154635


Typically, you won’t need to call logpost yourself, this will be done by the SMC sampler for you.

## IBIS¶

The IBIS (iterated batch importance sampling) algorithm is a SMC sampler that samples iteratively from a sequence of posterior distributions, $$p(\theta|y_{0:t})$$, for $$t=0,1,\ldots$$.

Module smc_samplers defines IBIS as a subclass of FeynmanKac.

[6]:

my_ibis = ssp.IBIS(my_static_model)
my_alg = particles.SMC(fk=my_ibis, N=1000, store_history=True)
my_alg.run()


Since we set store_history to True, the particles and their weights have been saved at every time (in attribute hist, see previous tutorials on smoothing). Let’s plot the posterior distributions of $$\mu$$ and $$\sigma$$ at various times.

[7]:

plt.style.use('ggplot')
for i, p in enumerate(['mu', 'sigma']):
plt.subplot(1, 2, i + 1)
for t in [100, 300, 900]:
plt.hist(my_alg.hist.X[t].theta[p], weights=my_alg.hist.wgt[t].W, label="t=%i" % t, alpha=0.5, density=True)
plt.xlabel(p)
plt.legend();


As expected, the posterior distribution concentrates progressively around the true values.

As before, once the algorithm is run, my_smc.X contains the N final particles. However, object my_smc.X is no longer a simple (N,) or (N,d) numpy array. It is a ThetaParticles object, with attributes:

• theta: a structured array: as mentioned above, this is an array with fields; i.e. my_smc.X.theta['mu'] is a (N,) array that contains the the $$\mu-$$component of the $$N$$ particles;
• lpost: a (N,) numpy array that contains the target (posterior) log-density of each of the N particles;
• acc_rates: a list of the acceptance rates of the resample-move steps.
[8]:

print(["%2.2f%%" % (100 * np.mean(r)) for r in my_alg.X.acc_rates])
plt.hist(my_alg.X.lpost, 30);

['23.33%', '12.70%', '26.07%', '31.82%', '32.75%', '31.90%', '35.50%', '35.98%', '33.75%', '35.28%']


You do not need to know much more about class ThetaParticles for most practical purposes (see however the documention of module smc_samplers if you do want to know more, e.g. in order to implement other classes of SMC samplers).

## Regarding the Metropolis steps¶

As the text output of my_alg.run() suggests, the algorithm “resample-moves” whenever the ESS is below a certain threshold ($$N/2$$ by default). When this occurs, particles are resampled, and then moved through a certain number of Metropolis-Hastings steps. By default, the proposal is a Gaussian random walk, and both the number of steps and the covariance matrix of the random walk are chosen automatically as follows:

• the covariance matrix of the random walk is set to scale times the empirical (weighted) covariance matrix of the particles. The default value for scale is $$2.38 / \sqrt{d}$$, where $$d$$ is the dimension of $$\theta$$.
• the algorithm performs Metropolis steps until the relative increase of the average distance between the starting point and the end point is below a certain threshold $$\delta$$.

Class IBIS takes as an optional argument mh_options, a dictionary which may contain the following (key, values) pairs:

• 'type_prop': either 'random walk' or 'independent’; in the latter case, an independent Gaussian proposal is used. The mean of the Gaussian is set to the weighted mean of the particles. The variance is set to scale times the weighted variance of the particles.
• 'scale’: the scale of the proposal (as explained above).
• 'nsteps': number of steps. If set to 0, the adaptive strategy described above is used.

Let’s illustrate all this by calling IBIS again:

[9]:

alt_ibis = ssp.IBIS(my_static_model, mh_options={'type_prop': 'independent',
'nsteps': 10})
alt_alg = particles.SMC(fk=alt_ibis, N=1000, ESSrmin=0.2)
alt_alg.run()


Well, apparently the algorithm did what we asked. We have also changed the threshold of Let’s see how the ESS evolved:

[10]:

plt.plot(alt_alg.summaries.ESSs)
plt.xlabel('t')
plt.ylabel('ESS')

[10]:

Text(0, 0.5, 'ESS')


As expected, the algorithm waits until the ESS is below 200 to trigger a resample-move step.

## SMC tempering¶

SMC tempering is a SMC sampler that samples iteratively from the following sequence of distributions:

$$\pi_t(\theta) \propto \pi(\theta) L(\theta)^\gamma_t$$

with $$0=\gamma_0 < \ldots < \gamma_T = 1$$. In words, this sequence is a geometric bridge, which interpolates between the prior and the posterior.

SMC tempering implemented in the same was as IBIS: as a sub-class of FeynmanKac, whose __init__ function takes as argument a StaticModel object.

[11]:

fk_tempering = ssp.AdaptiveTempering(my_static_model)
my_temp_alg = particles.SMC(fk=fk_tempering, N=1000, ESSrmin=1., verbose=True)
my_temp_alg.run()

t=0, ESS=500.00, tempering exponent=2.94e-05
t=1, Metropolis acc. rate (over 6 steps): 0.275, ESS=500.00, tempering exponent=0.000325
t=2, Metropolis acc. rate (over 6 steps): 0.261, ESS=500.00, tempering exponent=0.0018
t=3, Metropolis acc. rate (over 6 steps): 0.253, ESS=500.00, tempering exponent=0.0061
t=4, Metropolis acc. rate (over 6 steps): 0.287, ESS=500.00, tempering exponent=0.0193
t=5, Metropolis acc. rate (over 6 steps): 0.338, ESS=500.00, tempering exponent=0.0636
t=6, Metropolis acc. rate (over 6 steps): 0.347, ESS=500.00, tempering exponent=0.218
t=7, Metropolis acc. rate (over 5 steps): 0.358, ESS=500.00, tempering exponent=0.765
t=8, Metropolis acc. rate (over 5 steps): 0.366, ESS=941.43, tempering exponent=1


Note: Recall that SMC resamples every time the ESS drops below value N times option ESSrmin; here we set it to to 1, since we want to resample at every time. This makes sense: Adaptive SMC chooses adaptively the successive values of $$\gamma_t$$ so that the ESS drops to $$N/2$$ (by default).

Note: we use option verbose=True in SMC in order to print some information on the intermediate distributions.

We have not saved the intermediate results this time (option store_history was not set) since they are not particularly interesting. Let’s look at the final results:

[12]:

for i, p in enumerate(['mu', 'sigma']):
plt.subplot(1, 2, i + 1)
sb.distplot(my_temp_alg.X.theta[p])
plt.xlabel(p)


This looks reasonable! You can see from the output that the algorithm automatically chooses the tempering exponents $$\gamma_1, \gamma_2,\ldots$$. In fact, at iteration $$t$$, the next value for $$\gamma$$ is set that the ESS drops at most to $$N/2$$. You can change this particular threshold by passing argument ESSrmin to TemperingSMC. (Warning: do not mistake this with the ESSrmin argument of class SMC):

[13]:

lazy_tempering = ssp.AdaptiveTempering(my_static_model, ESSrmin = 0.1)
lazy_alg = particles.SMC(fk=lazy_tempering, N=1000, verbose=True)
lazy_alg.run()

t=0, ESS=100.00, tempering exponent=0.00097
t=1, Metropolis acc. rate (over 5 steps): 0.233, ESS=100.00, tempering exponent=0.0217
t=2, Metropolis acc. rate (over 6 steps): 0.323, ESS=100.00, tempering exponent=0.315
t=3, Metropolis acc. rate (over 5 steps): 0.338, ESS=520.51, tempering exponent=1


The algorithm progresses faster this time, but the ESS drops more between each step. Another optional argument for Class TemperingSMC is options_mh, which works exactly as for IBIS, see above. That is, by default, the particles are moved according to a certain (adaptative) number of random walk steps, with a variance calibrated to the particle variance.