lwiklendt

# testing effect of correlated predictors on non-linearity when assuming a linear model

rho = 0.9
N = 20
set.seed(1)

# generate correlated x1 and x2 predictors
z = matrix(rnorm(N*2, 0, 1), 2)
L = t(chol(matrix(c(1, rho, rho, 1), 2)))
x = L %*% z

lwiklendt

import numpy as np
from collections import OrderedDict

def pystan_vb_extract(results):
    param_specs = results['sampler_param_names']
    samples = results['sampler_params']
    n = len(samples[0])

    # first pass, calculate the shape
    param_shapes = OrderedDict()