{"articles":{"particle-filter@computational-statistical-methods":{"content":"<p>A particle filter is a sequential Monte Carlo method that has n sequences, X^(1), ..., X^(n). At each step a new observation is generated for and appended to each of the sequences.</p><p>At each step, a weighted sample is drawn. Then a resample is performed; this will prioritize.</p><p>This causes particles with low weights to be discarded, while high weight particles are replicated. The sampling step introduces variation between the particles.</p><p>Running the algorithm many steps should make the particles converge. <pre><code>fn particle_filter(initial_vals, t) {\n  n &lt;- initial_vals.len()\n  seqs &lt;- []\n  for i in 0..n {\n    seqs[i] &lt;- [initial_vals[i]]\n  }\n  for s in 0..t {\n\n  }\n}\n</code></pre></p>","names":[[["Particle filter",""]],[["Sequential importance resampling",""]]]}},"style":"Method"}