parameters[1, :] = sigma_v
return parameters
## Prior hyperparameters
hyperparameters = { \
"sigma_w_shape": 1, \
"sigma_w_scale": 1, \
"sigma_v_shape": 1, \
"sigma_v_scale": 1}
modeltheta = ParameterModel(name = "Periodic Gaussian model theta", dimension = 2)
modeltheta.setHyperparameters(hyperparameters)
modeltheta.setPriorlogdensity(logdprior)
modeltheta.setPriorgenerator(rprior)
modeltheta.setParameterNames(["expression(sigma[w]^2)", "expression(sigma[v]^2)"])
modeltheta.setTransformation(["log", "log"])
modeltheta.setRtruevalues([10, 1])
InverseGammaTemplate = """
priorfunction <- function(x){
shape <- %.5f
scale <- %.5f
return(scale**shape / gamma(shape) * x**(- shape - 1) * exp(-scale / x))
}
"""
modeltheta.setRprior([InverseGammaTemplate %
(hyperparameters["sigma_w_shape"], hyperparameters["sigma_w_scale"]), \
InverseGammaTemplate % (hyperparameters["sigma_v_shape"],
hyperparameters["sigma_v_scale"])])
""" returns untransformed parameters """
parameters = zeros((L, size))
parameters[0, :] = random.uniform(low=0,
high=hyperparameters["high"],
size=size)
parameters[1, :] = random.uniform(low=0,
high=hyperparameters["high"],
size=size)
#parameters[0, :] = random.exponential(scale = 1 / hyperparameters["b1_rate"], size = size)
#parameters[ell, :] = norm.rvs(size = size, loc = hyperparameters["b_mean"], scale = hyperparameters["b_sd"])
return parameters
## Prior hyperparameters
modeltheta = ParameterModel(name="logistic diffusion model theta", dimension=L)
modeltheta.setHyperparameters(hyperparameters)
modeltheta.setPriorlogdensity(logdprior)
modeltheta.setPriorgenerator(rprior)
modeltheta.setParameterNames(["tau, sigma"])
modeltheta.setTransformation(["log", "log"]) #(["none", "none"])
#modeltheta.setRprior({}) #seems useless
#modeltheta.setRprior(["priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"],\
# "priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["b_sd"]])
#modeltheta.setRprior(["priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"],\
# "priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"]])
set_argu = []
for m in range(L):
set_argu.append("priorfunction <- function(x) dexp(x, rate = %.5f)" %
hyperparameters["b1_rate"])
modeltheta.setRprior(set_argu)
""" Takes transformed parameters. When the parameter is transformed,
a jacobian appears in the formula.
"""
# the following is the log density of Y = logit(U) when U is Uniform(0,1)
rho_part = safelogdlogit(array([parameters[0]]))
return rho_part[0]
def rprior(size, hyperparameters):
""" returns untransformed parameters """
rho = random.uniform(size = size, low = 0.01, high = 0.99)
parameters = zeros((1, size))
parameters[0, :] = rho
return parameters
## Prior hyperparameters
modeltheta = ParameterModel(name = "Simplest model theta", dimension = 1)
modeltheta.setHyperparameters({})
modeltheta.setPriorlogdensity(logdprior)
modeltheta.setPriorgenerator(rprior)
modeltheta.setParameterNames(["expression(rho)"])
modeltheta.setTransformation(["logit"])
uniformprior = \
"""
priorfunction <- function(x){
return(1)
}
"""
modeltheta.setRprior([uniformprior])
parameters = zeros((L, size))
for ell in range(L):
##same as random.normal
#if ell==0:
parameters[ell, :] = random.exponential(scale = 1.0 / hyperparameters["b1_rate"], size = size)
#else:
# parameters[ell, :] = norm.rvs(size = size, loc = hyperparameters["b_mean"], scale = hyperparameters["b_sd"])
return parameters
## Prior hyperparameters
modeltheta = ParameterModel(name = "Nested LGSSM model theta", dimension = L)
modeltheta.setHyperparameters(hyperparameters)
modeltheta.setPriorlogdensity(logdprior)
modeltheta.setPriorgenerator(rprior)
modeltheta.setParameterNames(["expression(B)"])
#transType = ["log"]
transType = []
for m in range(L):
transType.append("log")
modeltheta.setTransformation(transType) #(["none", "none"])
#modeltheta.setRprior({}) #seems useless
#modeltheta.setRprior(["priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"],\
# "priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["b_sd"]])
#modeltheta.setRprior(["priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"],\
# "priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"]])
set_argu = []
for m in range(L):
set_argu.append("priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["b1_rate"])
modeltheta.setRprior(set_argu)
"mu_mean": 0, "mu_sd": sqrt(2), \
"beta_mean": 0, "beta_sd": sqrt(2), \
"xi_rate": 0.2, "omega2_rate": 0.2, \
"lambda1_rate": 1, "lambdaadd_rate": 0.5, \
"rho1_mean": 0, "rho1_sd": 5, \
"rho2_mean": 0, "rho2_sd": 5, }
modeltheta = ParameterModel(name = "SV multi-factor", dimension = 9)
modeltheta.setHyperparameters(hyperparameters)
modeltheta.setPriorlogdensity(logdprior)
modeltheta.setPriorgenerator(rprior)
modeltheta.setParameterNames(["expression(mu)", "expression(beta)", \
"expression(xi)", "expression(omega^2)", "expression(lambda[1])", "expression(lambda[2] - lambda[1])", \
"expression(w[1])", "expression(rho[1])", "expression(rho[2])"])
modeltheta.setTransformation(["none", "none", "log", "log", "log", "log", "logit", "none", "none"])
modeltheta.setRprior(["priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["mu_sd"], \
"priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["beta_sd"], \
"priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["xi_rate"], \
"priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["omega2_rate"], \
"priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["lambda1_rate"], \
"priorfunction <- function(x) dexp(x, rate = %.5f)" % hyperparameters["lambdaadd_rate"], \
"priorfunction <- function(x) 1", \
"priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["rho1_sd"], \
"priorfunction <- function(x) dnorm(x, sd = %.5f)" % hyperparameters["rho2_sd"]])