Most of my samples are repetitions, is there a way to give a weight to each sample that would represent how frequent it is so that the algorithm would only have to go through the unique set?
Or is there a way to manipulate the log(probability) function that I have defined to achieve this effect?
# simple example for data:
data = [(0,1,10), (0,2,10), (1,0,20), (1,0,20), (1,0,20), (0,0,49), (1,1,12)]
member_a = mc.Uniform('a', lower=-1.0, upper=0.0)
member_d = mc.Uniform('d', lower=-1.0, upper=0.0)
@mc.stochastic(observed=True, dtype=int)
def logLikelihood(value=data, a=member_a, d=member_d):
ratesMatrix = np.zeros((2,2))
ratesMatrix[0,0] = a
ratesMatrix[0,1] = -a
ratesMatrix[1,0] = -d
ratesMatrix[1,1] = d
r = []
t = []
for i in range(len(data)):
r.append(ratesMatrix[int(value[i][0]), int(value[i][1])])
t.append(value[i][2])
r = np.array(r, dtype=np.float64)
t = np.array(t, dtype=np.float64)
model = mc.MCMC([member_a,member_d,logLikelihood])
trace = model.sample(iter=5000)
