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Section: New Results

An increment type set-indexed Markov property

Participant : Paul Balança.

[1] investigates a new approach for the definition of a set-indexed Markov property, named 𝒞-Markov. The study is based on Merzbach and Ivanoff's set-indexed formalism, i.e. 𝒜 denotes a set-indexed collection and 𝒞 the family of increments C=AB, where A𝒜 and B𝒜(u) (finite unions of sets from 𝒜). Moreover, for any C=AB, B=i=1kAi, 𝒜𝐂 is defined as the following subset of 𝒜:

𝒜𝐂={U𝒜;UB}:={UC1,,UCp},wherep=|𝒜𝐂|

and 𝒜 corresponds to the semilattice {A1Ak,,A1A2,A1,Ak}𝒜. The notation 𝐗𝐂 refers to a random vector 𝐗𝐂=XUC1,,XUCp. Similarly, 𝐱𝐂 is used to denote a vector of variables (xUC1,,xUCp).

Then, an E-valued set-indexed process (XA)A𝒜 is said to be 𝒞-Markov with respect to a filtration (A)A𝒜 if it is adapted to (A)A𝒜 and if it satisfies

𝔼[f(XA)|𝒢C*]=𝔼[f(XA)|𝐗𝐂]-a.s.(7)

for all C=AB𝒞 and any bounded measurable function f:E𝐑. The sigma-algebra 𝒢C* is usually called the strong history of (A)A𝒜 and is defined as 𝒢C*=A𝒜,AC=A.

The 𝒞-Markov approach has several advantages compared to existing set-indexed Markov literature (mainly 𝒬-Markov described in [48] ). It appears to be a natural extension of the classic one-parameter Markov property. In particular, the concept of transition system can easily extended to our formalism: for any 𝒞-Markov process X, one can defined 𝒫={PC(𝐱𝐂;dxA);C𝒞} as

𝐱𝐂E|𝒜𝐂|,Γ;PC(𝐱𝐂;Γ):=(XAΓ|𝐗𝐂=𝐱𝐂).

A 𝒞-transition system 𝒫 happens to satisfy a set-indexed Chapman-Kolmogorov equation,

C𝒞,A'𝒜;PCf=PC'PC''fwhereC'=CA',C''=CA'(8)

and f is a bounded measurable function.

Similarly to the classic Markovian theory, is is proved in [1] that the initial distribution μ and 𝒫 characterize entirely the law of a 𝒞-Markov process, and that conversely, for any initial law and any 𝒞-transition system, a corresponding canonical set-indexed 𝒞-Markov process can be constructed. 𝒞-Markov processes enjoy several other properties such as

  1. Projections on elementary flows are Markovian;

  2. Conditional independence of natural filtrations;

  3. Strong Markov property.

The class of set-indexed Lévy processes defined and studied in [21] offers examples of 𝒞-Markov processes whose transition probabilities correspond to

C=AB𝒞,Γ;PC(𝐱𝐂;Γ)=μm(C)(Γ-ΔxB),(9)

where m is a measure on 𝒯 and μ the infinitely divisible probability measure that characterizes the Lévy process. We note that the transition system related the 𝒬-Markov property has a different form, even if it is related.

Another non-trivial example of 𝒞-Markov process is the set-indexed Ornstein-Uhlenbeck process that has been introduced and studied in [32] . It is a Gaussian Markovian process whose transition densities are given by

pC(𝐱𝐂;y)=1σC2πexp-12σC2y-e-λm(A)i=1n(-1)εixUCieλm(UCi)2,(10)

where λ and σ are positive parameters, m is a measure on 𝒯 and

σC2=σ22λ1-e-2λm(A)i=1n(-1)εie2λm(UCi).

In the particular case of multiparameter processes, corresponding to the indexing collection 𝒜={[0,t];t𝐑+N}, the 𝒞-Markov formalism is related to several existing works. It generalizes the two-parameter *-Markov property introduced in [53] and also embraces the multiparameter Markov property investigated recently in [68] . Finally, under some Feller assumption on the transition system, a multiparameter 𝒞-Markov process is proved to admit a modification with right-continuous sample paths.