# Probablistic Programming in Thirty Seconds

If you are already well-versed in probabalistic programming and just want to take a quick look at how Turing’s syntax works or otherwise just want a model to start with, we have provided a Bayesian coin-flipping model to play with.

This example can be run on however you have Julia installed (see Getting Started), but you will need to install the packages `Turing`

, `Distributions`

, `MCMCChains`

, and `StatsPlots`

if you have not done so already.

This is an excerpt from a more formal example introducing probabalistic programming which can be found in Jupyter notebook form here or as part of the documentation website here.

```
# Import libraries.
using Turing, StatsPlots, Random
# Set the true probability of heads in a coin.
p_true = 0.5
# Iterate from having seen 0 observations to 100 observations.
Ns = 0:100;
# Draw data from a Bernoulli distribution, i.e. draw heads or tails.
Random.seed!(12)
data = rand(Bernoulli(p_true), last(Ns))
# Declare our Turing model.
@model coinflip(y) = begin
# Our prior belief about the probability of heads in a coin.
p ~ Beta(1, 1)
# The number of observations.
N = length(y)
for n in 1:N
# Heads or tails of a coin are drawn from a Bernoulli distribution.
y[n] ~ Bernoulli(p)
end
end;
# Settings of the Hamiltonian Monte Carlo (HMC) sampler.
iterations = 1000
ϵ = 0.05
τ = 10
# Start sampling.
chain = sample(coinflip(data), HMC(iterations, ϵ, τ));
# Construct summary of the sampling process for the parameter p, i.e. the probability of heads in a coin.
psummary = Chains(chain[:p])
histogram(psummary)
```