The Move-to-Front (MtF) rule, also known as Tsetlin library or Heaps process, identifies a well–known stochastic process, which arises in various applied research areas. It is used to describe an experiment whereby objects are requested at a random instant with a certain probability from a finite set of items in a serial list; when an object is requested, it is moved to the front of the list and the positions of the remaining items are unchanged. This procedure defines an underlying Markov chain on the set of permutations and an important goal that has been pursued in this area is the determination of the probability distribution of the search cost, which is defined as the depth of the requested item in the list. The investigation of the MtF rule has attracted considerable interest of several authors working at the interface of Computer Science and Probability. For example,Fill (1996a, b) and Fill and Holst (1996) are important reference works in the area and investigated the model under different assumptions for the request probabilities. A noteworthy extension occurs when the number of items is infinite. In this context, Barrera and Paroissin (2004); Barrera et al. (2005) and Barrera et al. (2006)
studied the behaviour of the search cost distribution for random request probabilities defined by normalized positive independent and identically distributed (i.i.d.) random variables.Hattori and Hattori (2010) illustrate a link between sales ranks of online shops and the MtF rule. Jelenković (1999) considered different scaling limits for the search cost when suitable optimality conditions in the list are specified. In Barrera and Fontbona (2010)
the behaviour of the search cost distribution for an infinite number of objects is studied under the assumption that a law of large numbers is satisfied on the unnormalized weights that are used to identify the probability masses with which items are requested; additionally, some connections with results ofJelenković and Radovanović (2008) are displayed. Finally, it is worth remarking that the stationary distribution of the MtF Markov chain has been employed for modeling partial ranking data drawn from a finite collection of items. Such a model has been named after R.L. Plackett and R.D. Luce [see Plackett (1975) and Luce (1959)]. A nice recent application, within a Bayesian nonparametric framework, has been proposed in Caron, Teh and Murphy (2014). See also references therein.
The focus of the present paper will be on the determination of the stationary search cost distribution when the number of objects goes to infinity and the random request probabilities arise from the normalization of the increments of a subordinator. We will show that the limiting Laplace transform of the search cost distribution can be expressed in terms of the Laplace exponent of the underlying subordinator. As a by–product of this main result, we will be able to evaluate the moments of any order for the stationary search cost distribution. We will test our findings on the normalized generalized gamma process. Not only this includes as special cases other noteworthy instances of random discrete probability measures, such as the Dirichlet process and the normalized –stable subordinator, but it has also been extensively applied in the Bayesian nonparametric inference literature. See, e.g., Lijoi, Mena and Pruenster (2007). We will also provide a pointer to the two–parameter Poisson–Dirichlet process, also known as Pitman–Yor process (Pitman and Yor (1997)), thanks to its representation as a mixture of generalized gamma processes with a base measure having random total mass.
Outline of the paper
In Section 2 we will provide some preliminary notions about the stationary search cost distribution and recall a few relevant results which will be used in the paper. In Section 3 we will provide an expression for the limiting Laplace transform when an infinite–activity subordinator is used to model the request probabilities. As a consequence, we will be able to derive a general expression for the moments of any order of the search cost distribution. In Section 4 we will specify the moment formula when the request probabilities arise from normalization of a generalized gamma process or a two–parameter Poisson–Dirichlet process. As a byproduct, we will be able to recover results in Kingman (1975) and Leisen, Lijoi and Paroissin (2011) as special cases.
2 The stationary search cost distribution
In this Section we recall some preliminary definitions and results about the stationary search cost distribution. This part is also helpful since it sets up some notation that will be used henceforth. In order to describe the experiment that gives rise to the search cost distribution, consider a collection of items that are organized in a heap. For instance, books on a library shelf, files stored in a computer, etc. Suppose that the probability of requesting item is , . At each time, if is selected it is placed at the top of the heap. Successive requests are independent and, at each time, only one item may be removed from the heap. The underlying stochastic process is a Markov chain on permutations of the elements of the list and it is known as the Move-to-Front rule. See, e.g., Donnelly (1991). The stationary distribution of this Markov chain is
where is a random permutation of . In general, it is assumed that the chain starts deterministically in permutation.
The search cost is the position of the requested item in the heap or, equivalently, the number of items to be removed from the heap in order to find the requested one. In this setting, it might be of interest to determine the distribution of the search cost when the underlying Markov chain is at equilibrium. In order to do so, we assume that the probabilities are random. In particular, if is a sequence of independent random variables, one can define
In Kingman (1975) the ’s are expressed as the normalized increments of a stochastic process
where and : is a subordinator, i.e. a process with independent increments, almost surely increasing paths and such that . In particular, it is assumed that is a Gamma subordinator, i.e.
or a -stable subordinator for some , i.e.
In both cases, an expression of the expected stationary search cost has been determined in Kingman (1975), when the number of items is taken to diverge to in such a way that . Such a result has been extended by Lijoi and Pruenster (2004) to any subordinator and to the case where the list contains a finite number of items. In Leisen, Lijoi and Paroissin (2011) the limiting behaviour of the moments of any order of the stationary search cost distribution is investigated with a –stable subordinator defining the weights as in (1). Usually, when move-to-front processes are considered, the moves are done at each discrete unit of time. Nonetheless, for illustrative purposes it may be convenient to consider a continuous time specification of the process. For instance, Fill and Holst (1996) considered the case where the moves are done at the time points of a Poisson process of intensity on . This yields a continuized Markov chain, which has the same stationary distributions as the one arising in the discrete move–to–the–front case. In this setting, let
The search cost for item at time is defined as
Letting denote a random variable independent of the MtF Markov chain that identifies the label of the selected item, namely , then search cost at time equals The random variable
is termed the stationary search cost. It is worth noting that the stationary search cost can be seen as a size-biased pick from a size-biased permutation of a random discrete distribution on the positive integers minus one. To this end, the reader may refer to Perman, Pitman and Yor (1992) for a thorough investigation on size-biased permutations of ranked jumps of a subordinator; see also Pitman (2006). More recently, Pitman and Tran (2015) have investigated a finite dimensional analogue of the discrete random probability measure studied in Perman, Pitman and Yor (1992).
In Leisen, Lijoi and Paroissin (2011) one can find an explicit expression for the moments of any order of , when the request probabilities are the normalized increments of a –stable subordinator, as . In particular, one has
The main tool for achieving the previous result is the Laplace transform of , which can be determined according to the following
(Barrera and Paroissin (2004)) If are non–negative independent random variables and for each , then
for all , where and
In the next section we will tackle the problem of the determination of the moments by considering a more general setting.
3 Subordinators–based search cost distribution
Suppose is a stochastic process defined on some probability space such that
for any and , the random variables are independent ();
and for any ;
is right–continuous and non–decreasing, with –probability 1;
Henceforth is termed subordinator and there exists a measure on such that and
for any . The measure is often referred to as the Lévy measure of , whereas is the so–called Laplace exponent of . Noteworthy examples the Gamma process, which is identified by
and the –stable process, with , whose Lévy measure is
It is obvious that fully characterizes . Hence, in order to identify the cost search distribution, with request probabilities obtained as transformations of increments of subordinators, we can target the determination of the Laplace transform as a function of . Indeed, we provide a closed form expression for
and for the moments as , for any , in terms of the Laplace exponent of the underlying subordinator . In order to state the main result of the paper, it is convenient to introduce the following quantity,
for every .
If the are determined by normalizing the increments of a subordinator in (1), with for each , then
For any positive integer such that , , one has
where , , are the Stirling numbers of second kind and
If , for every , then one obviously has . If one, now, considers the expression of the Laplace transform in (4), it is apparent that , for any and
As one trivially has
then . On the other hand,
as , we conclude that
and the conclusion follows from the simple change of variable . So far, we have proved the weak convergence of to . As far as the determination of the moments of the limiting variable is concerned, note that the Laplace transform can be rewritten as
is equal in distribution to a mixture of Poisson distributions with parameterwhere is the mixing law. In view of this representation, one can determine the moments of as a mixture of moments of the underlying Poisson distributions. It is well known that the -th moment of a Poisson distribution with parameter is . This immediately yields that the -th moment of coincides with the right hand side of equation (10). According to the Corollary of Theorem 25.12 in Billingsley (1995), in order to establish the equality in equation (10), we need to prove that Following Leisen, Lijoi and Paroissin (2011), one has
The above equation suggests that it is enough to prove that , for every . Hence,
By using the well known inequality we get
and hence, the convergence in equation (10) follows from the assumption that . ∎
In the next Section we will use the previous result to derive the expression of the limiting moments when the ’s are obtained by means of a normalized generalized gamma process or of a two–parameter Poisson–Dirichlet process.
4 The generalized gamma process and the Two-parameter Poisson-Dirichlet process
The generalized gamma process has been introduced in Brix (1999) for constructing shot noise Cox processes. It is characterized by the following Lévy measure
where and . It turns out that the Laplace exponent, evaluated at any , is
In this case identifies the normalized generalized gamma process and it will be denoted with the notation NGG(
). This random probability measure has been used for density estimation in Bayesian nonparametric mixture models and, when its distribution is the directing measure of a sequence of exchangeable random elements, the associated predictive distributions can be determined in closed form. SeeLijoi, Mena and Pruenster (2007). It is also worth stressing that it includes as special cases both the normalized –stable () and the Dirichlet processes (). Furthermore, mixtures of normalized generalized gamma processes induce a two–parameter Poisson–Dirichlet process. Specifically, let be a random variable with density function
for any and , i.e. is a Gamma random variable with parameters and . If is a NGG() independent from , from Proposition 21 in Pitman and Yor (1997), one has that the normalized process has the same distribution as a Poisson–Dirichlet process with parameters . Hence, one can define weights
which, in turn, entail
As we will see in the proof of Theorem 4, marginalization with respect to allows to derive the monents of the stationary search cost distribution when the request probabilities come from a two–parameter Poisson–Dirichlet process.
If the are determined by normalizing the increments of a generalized gamma subordinator with for each , then
If are determined by the increments ’s in equation (14) then
First of all, note that, in both cases, it is immediate to check that , for every , with such that . From equation (11), it is easy to see that
A simple change of variable along with the integrability condition lead to
where is the incomplete gamma function, for any . From 0.160.2 in Gradshteyn and Ryzhik (2007) it follows that
Since , then is a polynomial in , i.e.
and this concludes the proof of equation (15). Equation (16) is proved by using the characterization of the two-parameter Poisson-Dirichlet process through mixture of generalized Gamma processes. It is straightforward to see that
Integrating over , and noting that
provides the desired result. ∎
Just to give an idea of the behaviour of the role of the parameters in determining the search cost distribution, we plot in Figure 1 the first and the second moment of the stationary search cost.
Remark: It is possible to recover the result in Leisen, Lijoi and Paroissin (2011) displayed in Theorem 1. Indeed, in equation (15) leads to their result. On the other hand, when goes to zero, it is easy to see, in equation (16), that
From the equation above, one can recover the result for provided in Kingman (1975).
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