How to learn a model from multiple observed sequences RRS feed

  • Question

  • Hi, this link introduces a HMM.

    The hyper-parameters (C, alpha, etc.) are pre-specified, and hence the a prior distribution of the parameters (A, pi, etc.). Given an observed sequence vector_x, the posterior distribution of the parameters (A, pi, etc.) can be inferred.

    What if we have multiple observed sequences? How can we improve the inference using multiple observations?

    I know in this paper mentioned an approach that to generate various hyper-parameters from different observations, then sample from each hyper-parameter set, and finally MLE the hyper-parameters from the samples. But this is to combine from appliance instances into an appliance type. That is, they combine (hyper-)parameters from different sources.

    In our case, we observe the same appliance multiple times, hence the inherent (hyper-)parameters are the same.

    In this case, is there any we to use multiple observations?

    One possible approach is to update the hyper-parameters sequentially using each observation: Let C_0 be the hyper-parameter for a prior before any data are provided. 1. to learn posterior C_1 from obs_0 and a prior C_0, then update a prior by C_1; 2. to learn posterior C_2 from obs_1 and a prior C_1, then update a prior by C_2; ...; M. to learn posterior C_M from obs_{M-1} and a prior C_{M-1}. Then let C_M be the final posterior.

    Is this a reasonable way to use multiple observations? Or is there any better way?


    Wednesday, June 3, 2015 11:33 PM

All replies

  • The best way to make use of multiple observed sequences is to change the model to have multiple sequences, provide all the data, and run inference once.
    Tuesday, June 9, 2015 5:25 AM