def get_prior():
prior_dict = {
'log_eta': dists.LogD(dists.Gamma(a=.1, b=.1)),
'logit_sigma': dists.LogitD(dists.Beta(a=1., b=1.)),
'log_c': dists.LogD(dists.Gamma(a=.1, b=.1))
}
return dists.StructDist(prior_dict)
def get_prior():
prior_dict = {
'log_eta': dists.LogD(dists.Gamma(a=.1, b=.1)), #W
'log_tau_minus_one': dists.LogD(dists.Uniform(a=0., b=4.)), #W
'logit_sigma': dists.LogitD(dists.Beta(a=1., b=1.)),
'log_c': dists.LogD(dists.Gamma(a=.1, b=.1)) #W
}
return dists.StructDist(prior_dict)
import particles
from particles import datasets as dts
from particles import distributions as dists
from particles import resampling as rs
from particles import smc_samplers as ssp
from particles import state_space_models
from particles.collectors import Moments
# data
data = dts.GBP_vs_USD_9798().data
# prior
dictprior = {
'mu': dists.Normal(scale=2.),
'sigma': dists.Gamma(a=2., b=2.),
'rho': dists.Beta(a=9., b=1.),
'phi': dists.Uniform(a=-1., b=1.)
}
prior = dists.StructDist(dictprior)
# moment function
def qtiles(W, x):
alphas = np.linspace(0.05, 0.95, 19)
return rs.wquantiles_str_array(W, x.theta, alphas=alphas)
# algorithms
N = 10**3 # re-run with N= 10^4 for the second CDF plots
fks = {}
fk_opts = {
label='filtered volatility')
plt.legend()
#results=particles.multiSMC(fk=fk_model,N=100,nruns=30,qmc={'SMC':False,'SQMC':True})
#results = particles.multiSMC(fk=fk_model, N=100, nruns=30, qmc={'SMC':False, 'SQMC':True})
#plt.figure()
#sb.boxplot(x=[r['output'].logLt for r in results], y=[r['qmc'] for r in results])
smooth_trajectories = pf.hist.backward_sampling(10)
plt.figure()
plt.plot(smooth_trajectories)
prior_dict = {
'mu': dists.Normal(),
'sigma': dists.Gamma(a=1., b=1.),
'rho': dists.Beta(9., 1.)
}
my_prior = dists.StructDist(prior_dict)
from particles import mcmc # where the MCMC algorithms (PMMH, Particle Gibbs, etc) live
pmmh = mcmc.PMMH(ssm_cls=StochVol,
prior=my_prior,
data=data,
Nx=50,
niter=1000)
pmmh.run() # Warning: takes a few seconds
plt.figure()
# plot the marginals
burnin = 100 # discard the 100 first iterations
for i, param in enumerate(prior_dict.keys()):