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#-----------------------------------------------------------------------------
# Name: Melding.py
# Purpose: Bayesian melding
#
# Author: Flvio Codeo Coelho
#
# Created: 2003/06/10
# RCS-ID: $Id: Melding.py $
# Copyright: (c) 2003
# Licence: GPL
# New field: Whatever
#-----------------------------------------------------------------------------
from Numeric import *
from RandomArray import *
from math import *
#from matplotlib.matlab import *
def Pooling(priors):
"""
Pooling(priors)
Returns the logarithmic pooling of priors.
priors is a list of one dimensional arrays (distributions)
"""
l = len(priors) # number of priors to pool
alfa = 1.0/float(l) # geometric pooling
print(alfa)
pw=[0]*l # initializing a list with l elements
for i in range(l):
pw[i] = priors[i]**alfa #weighting
p = product(pw)
p2 = p/sum(p) #renormalization
return p2
def invMod(q1,q1est,qtilphi):
"""
invMod(q1,q1est,qtilphi)
calculates and returns qtilteta from the three argumens above.
"""
l = len(q1est)
L = len(q1)
qtilteta = zeros(L, Float)
for i in range(l):
qtilteta[i] = qtilphi[i]*(q1[i]/q1est[i]) # Equation 13 on Poole & Raftery, 1998
qtilteta[L-1] = 1-sum(qtilteta)
return qtilteta
if __name__ == '__main__':
q1 = array([0.7,0.2, 0.1])
q2 = array([0.6,0.4])
q1est = array([.7,.3])
qtilphi=Pooling([q2,q1est])
print ['qtilphi = ',qtilphi]
qtilteta=invMod(q1,q1est,qtilphi)
print (qtilteta)
pass # add a call to run your script here
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