{"articles":{"sequential-monte-carlo@computational-statistical-methods":{"content":"<p>In sequential Monte Carlo the sample points themselves are generated in several steps. <br> Sequential Monte Carlo is useful when the sample points are high dimensional or sequences. The hope is that there is some structure within the sample points that makes them easier to sample stepwise. For example, a Markov chain or time series.</p><hr><p>A sequential Monte Carlo method generates for each t a sample of time series.</p><p>A sequential Monte Carlo method generates a sample in step \\(i\\), and uses this sample to generate a sample in step \\(i + 1\\).</p><p>This method is cheap to update: we simply append a new observation to the time series and update the weigth.</p>","names":[[["Sequential Monte Carlo",""]],[["Sequential Monte Carlo",""]]]}},"style":"Method"}