python - Custom priors in PyMC -


says that I will assign a custom to a and b < / Code> in PEMC, example:

p (a, b) one ???? (a + b) ^ (a ???? 5/2)

(For the inspiration behind this precedent earlier, see)

Is this PMC Can be done in? if so?

As an example, I would like to define the code below in the model below a and b .

PIMK # # as the # import code that defines the first: p (a, b) a ???? (A + b) ^ (one ???? 5/2) #. ("Pre", alpha = a, beta = b) # binomials that share a normal pre-compartment = dict () in xrange (nmgti): bins [i] = pm. Binomial ('bin_ {}' format. (I), p = theta, n = n_trials [i], value = n_yes [i], observed = true) mcmc = pm.MCMC ([bins, ps])

  1. Education must import so that I can correct manner from the constant ?
  2. In PyMC2, do I still need to adhere to Theano signaling?

  3. Finally, how can I tell my beta distribution later that alpha and beta Is Multivariate Distribution Before?

    Impo RT pymc.Multivariate.Continuous

    Category Custom Migrant (Continuous): "" "P (A, B) A ???? (A + B) ^ (A 5/2) 

     : Parameter: None: Support: 2 positive floats (parameter for beta distribution) "def" __init __ (self, mo, tau, * args , ** kwargs): super (CustomPrior, itself) .__ init __ (* args, ** kwargs) def logp (auto, A, B): return np.log (math.power (a + b), - 5 ./2)      
     
     
     in PEMMC2, move  a  and  b  together with the parameter:  
      # code that is defined first Does: p (a, b) a ???? (a + b) ^ (a ?? 5/2) @ pm.stochastic def ab (power = -2.5, value = [1,1]): if np.any (value & lt; = 0): return -np.inf Return Power * np.log (Value [0] + Value [1]) a = ab [0] b = ab [1]   
    .

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