Program for Sequential Monte Carlo Algorithm Does anybody has the
example of the program which
simulates Sequential Monte Carlo Algorithm?
In any software. I'm trying to write such
kind of program but there constantly are
question and problems I can't solve.
Regards
 A: One such library for Python is Qinfer. It's target audience is the field of quantum information processing. However, aside from the usage examples and a few of the built in models and distributions, it is completely general.
Below is the simple conjugate model you mentioned in the comments, which should work in a jupyter notebook. We do a single update specifying that 70 out of 100 flips are heads.
%matplotlib inline
import matplotlib.pyplot as plt
import qinfer as qi
import numpy as np

model = qi.BinomialModel(qi.CoinModel())
prior = qi.BetaDistribution(alpha=1, beta=1)
smc = qi.SMCUpdater(model, 10000, prior)

N = 100
d = N-70

# these experimental parameters seem cumbersome for simple models
eps = np.array([N]).astype(model.expparams_dtype)

# perform update step:
smc.update(d, eps)
smc.plot_posterior_marginal(range_min=0, range_max=1)


A: Have a look at a particle filter implemented in Matlab for estimating the state of Gaussian Switching Linear Dynamic System (SLDS):
https://www.mathworks.com/matlabcentral/fileexchange/62959-particle-filter
The sequential monte carlo algorithm is described in Nando de Freitas paper titled: Rao-Blackwellized particle filtering for fault diagnosis.
