Python 3.x 如果观测值是模糊规则的输出，如何计算后验概率

以下示例来自：

如果活动状态（观察到的变量）不是一个分类集，而是属于每个活动的模糊成员激活分布，我应该如何使用Bayespy？例如，与步行、清洁或购物等相互独立的活动状态不同，人们可以在模糊的框架中同时步行和购物，例如80%步行和20%清洁，因此：

• 我应该为我的模糊观察活动定义什么模型（高斯模型、分类模型、混合模型等）？i、 e.用什么来改变分类

activity=mixed（天气，分类，p）.random（）

a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
     [0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6

N = 100 # will be observing 100 samples

# discrete first-order Markov chain is constructed as
from bayespy.nodes import CategoricalMarkovChain
Z = CategoricalMarkovChain(a0, A, states=N)

# observations
# probability of each activity depends on the current weather
P = [[0.1, 0.4, 0.5], # walk, shop, clean for rainy weather
     [0.6, 0.3, 0.1]]

# observed process using P and chain Z
from bayespy.nodes import Categorical, Mixture
Y = Mixture(Z, Categorical, P)

# DATA

# generate artificial data from the model itself.
# Realisation of weather process
weather = Z.random()

# using weather process, draw realisations of activities
activity = Mixture(weather, Categorical, P).random()

# INFERENCE
#using weather and activities data, 
#we set our variable Y to be observed:
Y.observe(activity)

# variational Bayesian inference engine with all random variables
from bayespy.inference import VB
Q = VB(Y, Z)

# is only one unobserved random variable, we recover the exact posterior 
distribution
Q.update()