MatsuuraKentaro

data {
  int I; // number of data
  int R; // number of Rcen
  int F; // number of Freq
  array[I] int Rcen; // value of Rcen
  array[I] int Freq; // value of Freq
  array[I] int N;
  array[I] int PV;
}

MatsuuraKentaro

#include <ostream>

static int iteration_index = 1;

inline void add_iter(std::ostream* pstream__) {
  iteration_index += 1;
}

inline int get_iter(std::ostream* pstream__) {
  return iteration_index;

MatsuuraKentaro

library(cmdstanr)

waic <- function(log_likelihood) {
  training_error <- - mean(log(colMeans(exp(log_likelihood))))
  functional_variance_div_N <- mean(colMeans(log_likelihood^2) - colMeans(log_likelihood)^2)
  waic <- training_error + functional_variance_div_N
  return(waic)
}

model1_Si <- cmdstan_model('model1-addG-Simpson.stan')

MatsuuraKentaro

functions {
  // calculating the convolutions
  vector convolution(vector x, vector y_rev) {
    int T = num_elements(x);
    vector[T-1] res;
    for (t in 2:T) {
      res[t-1] = dot_product(x[1:(t-1)], y_rev[(T-t+2):T]);
    }
    return res;
  }

MatsuuraKentaro

MatsuuraKentaro

MatsuuraKentaro

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MatsuuraKentaro

library(ggjoy)
library(dplyr)
library(purrr)

# yutani data ------------------------------------------------------------
set.seed(10)
l <- rerun(26, rnorm(1000, mean = runif(1), sd = sqrt(runif(1))))
names(l) <- LETTERS
l[[2]] <- c(rnorm(500, mean = -1, sd = 0.3), rnorm(500, mean = 1, sd = 0.3))
d <- as.data.frame(l) %>% tidyr::gather(id, value)

MatsuuraKentaro

data {
  int Q;
  int N;
  vector[Q] D;
  int Solved[Q, N];
  vector[N] D_max;
  real D_range;
}

parameters {

MatsuuraKentaro

data {
  int I;
  int J;
  matrix[I,J] Y;
  real<lower=0> U;
}

parameters {
  matrix<lower=-5, upper=5>[I,J] mu;
  real<lower=0> s_mu;