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Here you can Sed posuere consectetur est at lobortis. Donec ullamcorper nulla non metus auctor fringilla. Maecenas sed diam eget risus varius blandit sit amet non magna. Donec id elit non mi porta gravida at eget metus. Praesent commodo cursus magna, vel scelerisque nisl consectetur et.

  <button onclick="sample_loop()">Start sampling</button>
  <button onclick="stop_sample_loop()">Stop sampling and plot histograms</button>
  <button onclick="clear_samples()">Clear samples</button>

<div id="mcmc_plots_div"> </div>
<div id='text_output_div'></div>
              //A small demo of https://github.com/rasmusab/bayes.js running a standard beta bernouli model. Just click "start sampling" to eh... start sampling...

// Feel free to change this model and/or data and see what happens! :) What distributions are available can be found here: https://github.com/rasmusab/bayes.js/blob/master/distributions.js

// Setting up the data, parameter definitions and the defining the log posterior
var data = {x: [1, 0, 1, 1, 0, 1, 1, 1]}
var params = {theta: {type: "real", lower: 0, upper: 1}}

var log_post = function(state, data) {
  // Start by defining a variable to hold the log posterior initialized to 0
  var log_post = 0;
  // Add the log densities of the priors to log_post.
  // If Priors are not specified that is the same as 
  // assuming flat uniform priors.
  log_post += ld.beta(state.theta, 2, 2);
  // Loop through the data and add each resulting log density to log_post.
  var n = data.x.length;
  for(var i = 0; i < n; i++) {
    log_post += ld.bern(data.x[i], state.theta)
  return log_post;

// Initializing the sampler and generate a sample of size 1000
var options = {thin: 2}  // Not really necessary to thin here, just showin' how...
var sampler = new mcmc.AmwgSampler(params, log_post, data, options);
var samples = sampler.sample(1);

//// Below is just the code to run the sampler and
//// to plot the samples. It's somewhat of a hack...

// Setting up the plots
var plot_margins =  {l: 40, r: 10, b: 40, t: 40, pad: 4}

var param_names = Object.keys(params);
var params_to_plot = Object.keys(params);

for(var i = 0; i < params_to_plot.length; i++) {
  var param = params_to_plot[i];
 $("div#mcmc_plots_div").append('<div>' +
    '<div id = "' + param + "_trace_div" + 
    '" style="width:350px;height:250px;display: inline-block;"></div>' +
    '<div id = "' +  param + "_hist_div" + 
    '" style="width:350px;height:250px;display: inline-block;"></div>' +
  Plotly.plot( $("div#" + param + "_trace_div")[0], [{y: samples[param] }], 
            {margin: plot_margins, title: "Traceplot of " + param});
  Plotly.plot( $("div#" +  param + "_hist_div")[0], [{x: samples[param], type: 'histogram' }], 
            {margin: plot_margins, title: "Posterior of " + param });

var update_trace_plots = function() {
  for(var i = 0; i < params_to_plot.length; i++) {
    var param = params_to_plot[i]
    Plotly.restyle($("div#" + param + "_trace_div")[0], {y: [samples[param]]})

var update_histograms = function() {
  for(var i = 0; i < params_to_plot.length; i++) {
    var param = params_to_plot[i]
    Plotly.restyle($("div#" + param + "_hist_div")[0], {x: [samples[param]], xbins: {}})

// Below are the functions that enables starting and stopping the 
// sampling using the buttons

var clear_samples = function() {
  samples = sampler.sample(1)

var sample_loop_timeout_id;
var sample_loop = function() {
  var n_samples = Math.min(250, Math.ceil(samples[param_names[0]].length / 10) );
  var more_samples = sampler.sample(n_samples);
  for(var i = 0; i < param_names.length; i++) {
    var param = param_names[i]
    Array.prototype.push.apply(samples[param], more_samples[param])
  sample_loop_timeout_id = setTimeout(sample_loop, 1)

var stop_sample_loop = function() {


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