Abstract: The method of the present invention is to modify an initial target distribution it ? by combining it with a point mass concentrated on an “artificial atom” ? 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 ? 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 ? are regeneration times. To recover an ergodic chain with limiting distribution ?, it is sufficient simply to delete every occurrence of the state ? from the new chain. The points immediately after the (deleted) occurrences of the state ? are then regeneration times in a Markov chain with limiting distribution ?.

Abstract: The method of the present invention is to modify an initial target distribution it by combining it with a point mass concentrated on an “artificial atom” &agr; 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 &agr; 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 &agr; are regeneration times. To recover an ergodic chain with limiting distribution &pgr;, it is sufficient simply to delete every occurrence of the state &agr; from the new chain. The points immediately after the (deleted) occurrences of the state &agr; are then regeneration times in a Markov chain with limiting distribution &pgr;.