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    Bayesian estimator




    Hello,
    can anyone please help with Bayesian estimatiors?
    If my model is a function of unknown parameters I am drawing inference about, say Weibull distribution with scale s and shape m:
    W(m,s),
    what is the proper Bayesian estimator for that model?

    I see two options:

    1) evaluate the model for point estimates (e.g. a posterior mean) of s,m
    W(E(m), E(s))

    or

    2) evaluate the expectation of the model as a multivariate function of random variables
    E(W(m,s))

    The second option seems correct from a math point of view but has some practical disadvantages. The properties of the original model are lost, i.e. the result is not a Weibull distribution anymore.

    Any help or hint to literature are very appreciated!

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    Re: Bayesian estimator

    Your question isn't clear to me. What are you trying to estimate? What is the question you're trying to answer?
    I don't have emotions and sometimes that makes me very sad.

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    Re: Bayesian estimator


    Given the model (Weibull distribution) and some data (from experiments), I am trying to infer on the model parameters. In the Bayesian inference, the parameters are treated as random variables. The question: Do I take point estimates of the parameters and apply them in the model or do I evaluate the an estimator of the model? I.e. do I treat the model as a function of random variables?

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