mike-lawrence

library(rstan)
library(plyr)
library(doMC)
options(cores=4) #set this appropriate to your system
registerDoMC()

#This example assumes you have the rats.txt and rats.stan example files in your working directory
# rats.txt: http://wiki.stan.googlecode.com/git/rstangettingstarted/rats.txt
# rats.stan: http://stan.googlecode.com/git/src/models/bugs_examples/vol1/rats/rats.stan

fonnesbeck

""" 
Implementation of kernel regression: 
""" 
 
from pylearn.old_dataset.learner import OfflineLearningAlgorithm 
from theano import tensor as T 
from theano.tensor.nnet import prepend_1_to_each_row 
from theano.scalar import as_scalar 
from common.autoname import AutoName 
import theano 

jrnold

// Local Level model in Stan
// parameterized using Matt's Trick
data {
  int n;
  vector[n] y;
  real<lower=0> theta1_mean;
  real<lower=0> theta1_sd;
}
parameters {
  real<lower=0> sigma_v;

khakieconomics

# Some fake data

library(dplyr); library(rstan)

# Write out the data generation with known parameters

# Set the number of individuals
n_ind <- 50