Hi @bbbales2, thank you very much for your kind reply.
I’m sorry that the way I asked caused a misunderstanding since my question was not about coding up a model like p(y_1 | \theta) p(y_2 | \theta) p(\theta).
Let me explain more detail about what I wanted to ask.
Let’s say we have a sensor that has three hidden states (y) and emit a value (X) every second depending on the state. We can get an observation sequence when we have an experiment.
As a result of N times independent experiments, we have obtained N observation sequences of different lengths as follows:
First time experiment: y1 = [1, 1, 2, 3], X1 = [0.3, 0.3, 1.2, 2.5]
Second time: y2 = [1, 2, 3, 3, 3], X2 = [0.2, 1.0, 2.2, 2.6, 2.5]
N time: yN = [1, 2, 2], XN = [0.1, 1.0, 0.9]
What I’d like to realize is to train a HMM using these N observation sequences so that making a hidden state estimator using a sequence of sensor values.
I’m very sorry to bother you but could you give me an example code that realizes things above?