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我有一个多元蒙特卡罗隐马尔可夫问题要解决：

   x[k] = f(x[k-1]) + B u[k]
   y[k] = g(x[k])

在哪里：

x[k] the hidden states (Markov dynamics)
y[k] the observed data
u[k] the stochastic driving process

PyMC3 是否已经足够成熟来处理这个问题，还是我应该继续使用 2.3 版？其次，任何对 PyMC 框架中 HM 模型的引用都将不胜感激。谢谢。

——亨克

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1 回答 1

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我对 PyMC 2.x 做了类似的事情。我的你不是时间依赖的。这是我的例子。

# we're using `some_tau` for the noise throughout the example.
# this should be replaced with something more meaningful.
some_tau = 1 / .5**2

# PRIORS
# we don't know too much about the velocity, might be pos. or neg. 
vel = pm.Normal("vel", mu=0, tau=some_tau)

# MODEL
# next_state = prev_state + vel (and some gaussian noise)
# That means that each state depends on the prev_state and the vel.
# We save the states in a list.
states = [pm.Normal("s0", mu=true_positions[0], tau=some_tau)]
for i in range(1, len(true_positions)):
    states.append(pm.Normal(name="s" + str(i),
                            mu=states[-1] + vel,
                            tau=some_tau))

# observation with gaussian noise
obs = pm.Normal("obs", mu=states, tau=some_tau, value=true_positions, observed=True)

我想您需要将 vel 建模为 RV 列表。他们也可能有一些依赖性。

这是原始问题： PyMC：马尔可夫系统中的参数估计

这是作为 IPython 笔记本的完整示例：http: //nbviewer.ipython.org/github/sotte/random_stuff/blob/master/PyMC%20-%20Simple%20Markov%20Chain.ipynb

于 2013-11-29T14:58:36.610 回答