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STS555 Patrice Bertail et al.
                     In short, Harris recurrence property ensures that X visits set A infinitely often a.s..
                  It  is  well-known  that  in  Harris  recurrent  case  it  is  also  possible  to  recover  the
                  regeneration properties via splitting technique introduced in [5].

                  Definition 3. We say that a set S ∈ E is small if there exists a parameter δ > 0,

                  a positive probability measure Φ supported by S and an integer m ∈ N such
                                                                                       ∗
                  that



                  where Π denotes the m-th iterate of the transition probability Π.
                          m
                     The inequality (1) from above definition is called minorization condition
                  M(m,  S,  δ,  ψ)  and  gives  a  uniform  bound  from  below  on  the  transition
                  probabilities. Note that the parameter δ controls how fast our chain X forgets
                  its past.
                     It  is  assumed  throughout  the  rest  of  this  paper  that  the  minorization
                  condition M is satisfied with m = 1. The family of the conditional distributions
                  {Π(x, dy)}x∈E and the initial distribution ν are dominated by a σ-finite measure λ of
                  reference, so that ν(dy) = f (y)λ(dy) and Π(x, dy) = p(x, y)λ(dy), for all x ∈ E and
                  that p(x, y) ≥ δφ(y), λ(dy) a.s. for any x ∈ S, with Φ(dy) = φ(y)dy. We then split the
                  series as in the same way as it was originally done in [1].

                  Algorithm (Approximate regeneration blocks construction)
                     1. Construct  an  estimator  (may  be  on  part  of  the  data)  pn(x,  y)  of  the
                       transition  density  using  sample  Xn+1.  An  estimator  pn  must  satisfy  the
                       following conditions

                            pn(x, y) ≥ δγ(y), λ(dy) a.s. and pn(Xi, Xi+1) > 0, 1 ≤ i ≤ n.

                                                 ˆ
                     2. Conditioned on Xn+1 draw Y ’s only at those time points when Xi ∈ S. That
                       is because only then the split chain can regenerate. At such time point i,
                            ˆ
                       draw  Yi from the Bernoulli distribution with parameter δγ(Xi+1)\pn(Xi, Xi+1).
                                                 ˆ
                                                                    ˆ
                     3. Count the number of visits  ln = ∑   "{Xi ∈ S,  Yi = 1) to the atom S1 = S
                                                       =1
                       × {1} up to time n.
                                                      ˆ
                     4. Divide the trajectory Xn+1  into  ln + 1 approximate regeneration blocks
                                                                ˆ
                       according to the consecutive visits of (X, Y ) to S1. Approximated blocks
                       are of the form





                       where


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