def getReferencePriorPP(self): if self.observations != None: loc = self.sampleMean scale = ((1+self.n)*self.sampleVariance)/(self.n*(self.n-1)) df = self.n-1 return scipyStudent(df=df,loc=loc,scale=np.sqrt(scale)) else: return None
def getPPTom(self): "This is the posterior predictive distribution as defined in Griffiths & Sanborn's notation - notation is the only difference from Murphy's definition." "Returns a frozen scipy t distribution object parametrised according to computed posterior quantities" ui,lambdai,ai,sigmai = self.translatePosteriorQuantitiesTom() loc, scale, df = ui, sigmai*(1+(1./lambdai)), ai precisionLambda = 1./pow(scale,2) #NOTE: Scipy's distribution object assumes sqrt of variance (e.g. std in the Normal case) #Therefore must use np.sqrt of whatever quantity the model gives us for the scale param of a t dist #PYMC Version: student(mu=ui,lam=precisionLambda,nu = ai) return scipyStudent(df=df,loc=loc,scale=np.sqrt(scale))
def getPP(self): "This is the posterior predictive distribution as defined in KMurphy's ConjAnalysis Doc" "Returns a frozen scipy t distribution object parametrised according to computed posterior quantities" "The PP in this model is a non-standard t distribution with df = vn, and shifted loc = un, scale = ((1+kn)*sigman)/kn " if self.observations != None: loc,scale, df = self.un, ((1+self.kn)*self.sigman)/self.kn, self.vn precisionLambda = 1./pow(scale,2) #PYMC Version: student(mu=loc,lam=precisionLambda,nu = df) #NOTE: Scipy's distribution object assumes sqrt of variance (e.g. std in the Normal case) #Therefore must use np.sqrt of whatever quantity the model gives us for the scale param of a t dist return scipyStudent(df=df,loc=loc,scale=np.sqrt(scale)) else: return None
