METHOD AND SYSTEM FOR IDENTIFYING REGENERATION POINTS IN A MARKOV CHAIN MONTE CARLO SIMULATION

The method of the present invention is to modify an initial target distribution it π by combining it with a point mass concentrated on an “artificial atom” α which is outside the state-space X. A Markov chain may then be constructed using any known technique (for example, using the Metropolis-Hastings Algorithm) with the new target distribution. For this chain, the state α is Harris-recurrent (i.e. with probability one, it occurs infinitely many times). By the Markov property, the times at which the new chain hits α are regeneration times. To recover an ergodic chain with limiting distribution π, it is sufficient simply to delete every occurrence of the state α from the new chain. The points immediately after the (deleted) occurrences of the state α are then regeneration times in a Markov chain with limiting distribution π.