A Simple Markow Chain
Let's say we want to estimate parameters of a system such that we can predict the state of the system at timestep t+1 given the state at timestep t. PyMC should be able to deal with this easily.
Let our toy system consist of a moving object in a 1D world. The state is the position of the object. We want to estimate the latent variable/the speed of the object. The next state depends on the previous state and the latent variable the speed.
# define the system and the data
true_vel = .2
true_pos = 0
true_positions = [.2 * step for step in range(100)]
We assume that we have some noise in our observation (but that does not matter here).
The question is: how do I model the dependency of the next state on the current state. I could supply the transition function a parameter idx to access the position at time t and then predict the position at time t+1.
vel = pymc.Normal("pos", 0, 1/(.5**2))
idx = pymc.DiscreteUniform("idx", 0, 100, value=range(100), observed=True)
@pm.deterministic
def transition(positions=true_positions, vel=vel, idx=idx):
return positions[idx] + vel
# observation with gaussian noise
obs = pymc.Normal("obs", mu=transition, tau=1/(.5**2))
However, the index seems to be an array which is not suitable for indexing. There is probably a better way to access the previous state.
The easiest way is to generate a list, and allow PyMC to deal with it as a Container. There is a relevant example on the PyMC wiki. Here is the relevant snippet:
# Lognormal distribution of P's
Pmean0 = 0.
P_0 = Lognormal('P_0', mu=Pmean0, tau=isigma2, trace=False, value=P_inits[0])
P = [P_0]
# Recursive step
for i in range(1,nyears):
Pmean = Lambda("Pmean", lambda P=P[i-1], k=k, r=r: log(max(P+r*P*(1-P)-k*catch[i-1],0.01)))
Pi = Lognormal('P_%i'%i, mu=Pmean, tau=isigma2, value=P_inits[i], trace=False)
P.append(Pi)
Notice how the mean of the current Lognormal is a function of the last one? Not elegant, using list.append
and all, but you can use a list comprehension instead.
来源:https://stackoverflow.com/questions/20222971/pymc-parameter-estimation-in-a-markov-system