def getYDensity(y, p1, p2, whichModel, v=0):
if whichModel == 0:
return util.gaussianDensity(y, 0, 1)
elif whichModel == 1:
return util.betaDensity(y, p1, p2)
elif whichModel == 2:
return util.gammaDensity(y, p2, p2)
elif whichModel == 3:
return util.gaussianDensity(y, 0, 1)
elif whichModel == 4:
return util.gaussianDensity(y, 0, 1)
elif whichModel == 5:
return util.weibullDensity(y, p1, p2)
elif whichModel == 6:
return util.gaussianDensity(y, 0, 1) * util.chi2Density(v, p2)
def jointIntegrandT(g,w,p,q,myRho,nu):
p1 = computeP_t(p,myRho,g,w,nu)
p2 = computeP_t(q,myRho,g,w,nu)
density1 = util.gaussianDensity(g,0,1)
density2 = util.chi2Density(w,nu)
f = p1*p2*density1*density2
return f
def logitProbitMoment(z, mu, sigma, momentNumber, isLogit):
if isLogit == 1:
v0 = (np.reciprocal(1 + np.exp(-(mu + sigma * z))))**momentNumber
else:
v0 = (norm.cdf(mu + sigma * z))**momentNumber
density = util.gaussianDensity(z, 0, 1)
f = v0 * density
return f
def jointIntegrandNVM(g,v,p,q,myRho,myA,invCdf,whichModel):
p1 = computeP_NVM(p,myRho,g,v,myA,invCdf)
p2 = computeP_NVM(q,myRho,g,v,myA,invCdf)
density1 = util.gaussianDensity(g,0,1)
if whichModel==0:
density2 = util.gammaDensity(v,myA,myA)
elif whichModel==1:
density2 = util.gigDensity(v,myA)
return p1*p2*density1*density2
def thresholdMoment(g, w, p1, p2, myP, whichModel, myMoment, invCdf=0):
d1 = util.gaussianDensity(g, 0, 1)
if whichModel == 1: # t
d2 = util.chi2Density(w, p2)
integrand = np.power(th.computeP_t(myP, p1, g, w, p2), myMoment)
if whichModel == 2: # Variance-gamma
d2 = util.gammaDensity(w, p2, p2)
integrand = np.power(th.computeP_NVM(myP, p1, g, w, p2, invCdf),
myMoment)
if whichModel == 3: # Generalized hyperbolic
d2 = util.gigDensity(w, p2)
integrand = np.power(th.computeP_NVM(myP, p1, g, w, p2, invCdf),
myMoment)
return integrand * d1 * d2
def jointIntegrandRegion(g, p, q, rhoVec):
p1 = th.computeP(p, rhoVec[0], g)
p2 = th.computeP(q, rhoVec[1], g)
density = util.gaussianDensity(g, 0, 1)
f = p1 * p2 * density
return f
def integrateGaussianMoment(g, r, myP, myMoment):
integrand = np.power(th.computeP(myP, r, g), myMoment)
return integrand * util.gaussianDensity(g, 0, 1)
def jointIntegrand(g, p, q, myRho):
p1 = th.computeP(p, myRho, g)
p2 = th.computeP(q, myRho, g)
f = p1 * p2 * util.gaussianDensity(g, 0, 1)
return f
def getCMF2r(g, myRho, pVec3, nVec3, kVec3):
myF = getProdCMF(g, myRho, pVec3, nVec3, kVec3)
return np.prod(myF) * util.gaussianDensity(g, 0, 1)
def getCMF(g, myRho, myP, myN, myK):
pg = th.computeP(myP, myRho, g)
f = util.getBC(myN, myK) * np.power(pg, myK) * np.power(1 - pg, myN - myK)
cmf = f * util.gaussianDensity(g, 0, 1)
return cmf
def jointIntegrandG(g,p,q,myRho):
p1 = computeP(p,myRho,g)
p2 = computeP(q,myRho,g)
density = util.gaussianDensity(g,0,1)
f = p1*p2*density
return f