\[%% % Add your macros here; they'll be included in pdf and html output. %% \newcommand{\R}{\mathbb{R}} % reals \newcommand{\E}{\mathbb{E}} % expectation \renewcommand{\P}{\mathbb{P}} % probability \]

The date today is Fri Jan 5 17:04:47 2018. Here is a plot of a spiral.

tt <- seq(0, 20*pi, length.out=400)
plot(1.01^seq_along(tt) * cos(tt), 1.01^seq_along(tt) * sin(tt), type='l')

plot of chunk spiral_plot

Here we use Stan to infer the mean of an inverse Exponential.

library(rstan)
## Loading required package: ggplot2
## Loading required package: StanHeaders
## rstan (Version 2.16.2, packaged: 2017-07-03 09:24:58 UTC, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## rstan_options(auto_write = TRUE)
## options(mc.cores = parallel::detectCores())
stan_block <- "
data {
    int N;
    vector<lower=0>[N] x;
}
transformed data {
    vector[N] y;
    y = 1.0 ./ x;
}
parameters {
    real<lower=0> beta;
}
transformed parameters {
}
model {
    y ~ exponential(beta);
}
generated quantities {
}
"
fit <- stan(model_code=stan_block,
            data=list(x=1/rexp(100), N=100),
            chains=2, iter=1000)
## 
## SAMPLING FOR MODEL 'bcb93c279f8057c9c1dcfbef5e0d54d3' NOW (CHAIN 1).
## 
## Gradient evaluation took 4e-06 seconds
## 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
## Adjust your expectations accordingly!
## 
## 
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## 
##  Elapsed Time: 0.004745 seconds (Warm-up)
##                0.003812 seconds (Sampling)
##                0.008557 seconds (Total)
## 
## 
## SAMPLING FOR MODEL 'bcb93c279f8057c9c1dcfbef5e0d54d3' NOW (CHAIN 2).
## 
## Gradient evaluation took 1e-06 seconds
## 1000 transitions using 10 leapfrog steps per transition would take 0.01 seconds.
## Adjust your expectations accordingly!
## 
## 
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## 
##  Elapsed Time: 0.004309 seconds (Warm-up)
##                0.004206 seconds (Sampling)
##                0.008515 seconds (Total)
summary(fit)
##  Length   Class    Mode 
##       1 stanfit      S4