On the connection between probability boxes and possibility measures

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On the connection between probability boxes and possibility measures

Troffaes Matthias, Enrique Miranda, Sébastien Destercke

To cite this version:

Troffaes Matthias, Enrique Miranda, Sébastien Destercke. On the connection between probability boxes and possibility measures. The 7th conference of the European Society for Fuzzy Logic and Tech- nology (EUSFLAT-2011), Jul 2011, Aix-les-Bains, France. pp.836 - 843, �10.2991/eusflat.2011.150�.

�hal-00655597�

On the connection between probability boxes and possibility measures

Matthias C. M. Troffaes¹Enrique Miranda²Sebastien Destercke³

1Durham University, Durham, United Kingdom

2University of Oviedo, Oviedo, Spain

3UMR1208, French Agricultural Research Centre for International Development, Montpellier, France

Abstract

We explore the relationship between p-boxes on totally preordered spaces and possibility measures. We start by demonstrating that only those p-boxes who have 0–1- valued lower or upper cumulative distribution function can be possibility measures, and we derive expressions for their natural extension in this case. Next, we estab- lish necessary and sufficient conditions for a p-box to be a possibility measure. Finally, we show that almost every possibility measure can be modelled by a p-box.

Whence, any techniques for p-boxes can be readily ap- plied to possibility measures. We demonstrate this by deriving joint possibility measures from marginals, un- der varying assumptions of independence, using a tech- nique known for p-boxes.

Keywords: Possibility measures, maxitive measures, p- boxes, coherent upper previsions, natural extension.

1. Introduction

Possibility measures [1] are supremum preserving set functions, and are widely applied in many fields, in- cluding data analysis [2], cased-based reasoning [3], and psychology [4]. In this paper we are concerned with quantitative possibility theory [5], where degrees of pos- sibility range in the unit interval. Their interpretation as upper probability [6, 7] fits our purpose best.

Probability boxes [8], or p-boxes for short, are pairs of lower and upper cumulative distribution functions, and are often used in risk and safety studies, in which cumu- lative distributions play an essential role. P-boxes have been connected to info-gap theory [9], random sets [10], and also, partly, to possibility measures [11]. P-boxes can be defined on arbitrary finite spaces [12], and, more generally, even on arbitrary totally preordered spaces [13]—we will use this extensively.

This paper aims to consolidate the connection be- tween possibility measures and p-boxes, making as few assumptions as possible. We prove that almost every possibility measure can be interpreted as a p-box. Con- versely, we provide necessary and sufficient conditions for a p-box to be a possibility measure.

To study this connection, we use imprecise proba- bilities [14], of which both possibility measures and p- boxes are particular cases. Possibility measures are ex- plored as imprecise probabilities in [6, 7, 15], and p- boxes are studied as imprecise probabilities briefly in

[14, Section 4.6.6] and [16], and in much more detail in [13].

The paper is organised as follows: in Section 2, we give the basics of the behavioural theory of imprecise probabilities, and recall some facts about p-boxes and possibility measures; in Section 3, we first determine necessary and sufficient conditions for a p-box to be maximum preserving, before determining in Section 4 necessary and sufficient conditions for a p-box to be a possibility measure; in Section 5, we show that almost any possibility measure can be seen as particular p-box, and that many p-boxes can be seen as a couple of possi- bility measures; some special cases are detailed in Sec- tion 6. Finally, in Section 7 we apply the work on mul- tivariate p-boxes from [13] to derive multivariate possi- bility measures from given marginals, and in Section 8 we give a number of additional comments and remarks.

Note that proofs are omitted for brevity, whence be- ware that results are presented in the order that they ap- pear most logical, and not in the order that they are most easily proven.

2. Preliminaries

2.1. Imprecise Probabilities

We briefly introduce imprecise probabilities; see [17, 18, 14, 19] for more details.

LetΩbe a possibility space. A subset ofΩis called anevent. Denote the set of all events by℘(Ω), and the set of all finitely additive probabilities on℘(Ω)byP.

Anupper probabilityis any real-valued functionPde- fined on an arbitrary subsetK of℘(Ω). With P, we associate alower probability P on {A:A^c∈K} via P(A) =1−P(A^c). Consider the set

M(P) ={P∈P:(∀A∈K)(P(A)≤P(A))}.

The upper envelope E of M(P) is called the natural extension[14, Thm. 3.4.1] ofP:

E(A) =sup{P(A):P∈M(P)}

for allA⊆Ω. The corresponding lower probability is denoted byE, so E(A) =1−E(A^c). Clearly,E is the lower envelope ofM(P).

We say thatPiscoherent(see [14, Sec. 3.3.3]) when, for allA∈K,

P(A) =E(A).

Pis calledcoherentwheneverPis.

1 Ω 0

1

F F

Figure 1: Example of a p-box on[0,1].

2.2. P-Boxes

In this section, we revise p-boxes defined on totally pre- ordered (not necessarily finite) spaces. For further de- tails, see [13].

Start with a totally preordered space(Ω,). So,is transitive, reflexive and any two elements are compara- ble. As usual, we writex≺yforxyandx6y,xy fory≺x, andx'yforxyandyx. For any twox, y∈Ωexactly one ofx≺y,x'y, orxyholds. We also use the following common notation for intervals in Ω:

[x,y] ={z∈Ω:xzy}

(x,y) ={z∈Ω:x≺z≺y}

and similarly for[x,y)and(x,y].

We assume thatΩhas a smallest element 0_Ω and a largest element 1_Ω—we can always add these two ele- ments to the spaceΩ.

A cumulative distribution function is a non- decreasing mapF :Ω→[0,1] for which F(1_Ω) =1.

For eachx∈Ω,F(x) is interpreted as the probability of[0_Ω,x].

The quotient set ofΩwith respect to'is denoted by Ω/':

[x]_'={y∈Ω:y'x}for anyx∈Ω Ω/'={[x]':x∈Ω}.

BecauseFis non-decreasing,Fis constant on elements [x]_'ofΩ/'.

Definition 1. A probability box, or p-box, is a pair (F,F) of cumulative distribution functions fromΩ to [0,1]satisfyingF≤F.

A p-box is interpreted as a lower and an upper cumu- lative distribution function (see Fig. 1), or more specifi- cally, as an upper probabilityP_F,Fon the set of events

{[0_Ω,x]:x∈Ω} ∪ {(y,1_Ω]:y∈Ω}

defined by

P_F,F([0_Ω,x]) =F(x)andP_F,F((y,1_Ω]) =1−F(y).

We denote byE_F,Fthe natural extension ofP_F,Fto all events. To simplify the expression for natural extension, we introduce an element 0_Ω−such that:

0_Ω− ≺xfor allx∈Ω F(0_Ω−) =F(0_Ω−) =F(0_Ω−) =0.

Note that(0_Ω−,x] = [0_Ω,x]. Now, letΩ^∗=Ω∪ {0_Ω−}, and define

H ={(x₀,x1]∪(x₂,x3]∪ · · · ∪(x_2n,x2n+1]: x₀≺x₁≺ · · · ≺x_2n+1inΩ^∗}.

Proposition 2. [13, Prop. 4] For any A∈H, that is A= (x₀,x₁]∪(x₂,x₃]∪ · · · ∪(x_2n,x_2n+1]with x₀≺x₁≺ · · · ≺ x_2n+1inΩ^∗, it holds that E_F,F(A) =P^H_F,F(A), where

P^H_F,F(A) =1−

n+1 k=0

∑

max{0,F(x_2k)−F(x2k−1)}, (1)

with x−1=0_Ω−and x_2n+2=1_Ω.

To calculate E_F,F(A) for an arbitrary event A⊆Ω, use the outer measure [14, Cor. 3.1.9]P^H_F,F^∗of the upper probabilityP^H_F,Fdefined in Eq. (1):

E_F,F(A) =P^H_F,F^∗(A) = inf

C∈H,A⊆CP^H_F,F(C). (2) For intervals, we immediately infer from Proposi- tion 2 and Eq. (2) that (‘i.p.’ stands for ‘immediate pre- decessor’)

E_F,F((x,y]) =F(y)−F(x) (3a) E_F,F([x,y]) =F(y)−F(x−) (3b) E_F,F((x,y)) =

(F(y)−F(x) ifyhas no i.p.

F(y−)−F(x) ifyhas an i.p. (3c) E_F,F([x,y)) =

(F(y)−F(x−) ifyhas no i.p.

F(y−)−F(x−) ifyhas an i.p.

(3d) for anyx≺yinΩ,¹whereF(y−)denotes sup_z≺yF(z) and similarly forF(x−). IfΩ/'is finite, then one can think ofz−as the immediate predecessor ofzin the quo- tient spaceΩ/'for anyz∈Ω. We also have that

E_F,F({x}) =F(x)−F(x−) (4) for anyx∈Ω.

2.3. Possibility and Maxitive Measures Brevity is stipulated; see [1, 5, 6, 7] for details.

Definition 3. Amaxitive measureis an upper probabil- ityPon℘(Ω)satisfyingP(A∪B) =max{P(A),P(B)}

for everyAandB⊆Ω.

Proposition 4. [16, Def. 3.22, Thm. 3.46] A maxitive measure P is coherent whenever P(/0) =0and P(Ω) =1.

Possibility measures are a particular case of maxitive measures.

1In casex=0_Ω, evidently, 0_Ω−is the immediate predecessor.

Definition 5. A (normed) possibility distribution is a mappingπ: Ω→[0,1]satisfying sup_x∈Ωπ(x) =1. A possibility distributionπ induces apossibility measure Πon℘(Ω), given by:

Π(A) =sup

x∈A

π(x)for allA⊆Ω.

If we writeE_Πfor the conjugate lower probability of the upper probabilityΠ, then:

E_Π(A) =1−Π(A^c) =1−sup

x∈A^c

π(x).

A possibility measure is maxitive, but not all maxitive measures are possibility measures.

As an imprecise probability model, possibility mea- sures are not as expressive as for instance p-boxes. This poor expressive power is also illustrated by the fact that, for any eventA:

Π(A)<1 =⇒ E_Π(A) =0, and E_Π(A)>0 =⇒ Π(A) =1,

meaning that every event has a trivial probability bound on at least one side. Their main attraction is that cal- culations with them are very easy: to find the upper (or lower) probability of any event, a simple supremum suf- fices.

In the following sections, we characterize the circum- stances under which a possibility measureΠis the nat- ural extension of some p-box(F,F). In order to do so, we first characterise the conditions under which a p-box induces a maxitive measure.

3. P-boxes as Maxitive Measures

3.1. Necessary and Sufficient Condition for Maxitivity

Let us begin by characterising under which conditions the natural extension of a p-box is maxitive:

Theorem 6. The natural extension E_F,F of a p-box (F,F)is maximum preserving if and only if at least one of F or F is0–1-valued.

Hence, for the purposes of this paper we can restrict our attention to p-boxes(F,F)where at least one ofF orFis 0–1-valued. Next, we provide expressions for the natural extensions of such p-boxes, and determine under which conditions these are possibility measures.

3.2. Natural Extension of Maxitive P-Boxes

In case of 0–1-valuedF, we arrive at the following ex- pression:

Proposition 7.Let(F,F)be a p-box with0–1-valued F, and let B={x∈Ω^∗:F(x) =0}. Then, for any A⊆Ω,

E_F,F(A) =min

y∈B^c inf

x∈Ω^∗:A∩[0_Ω,y]xF(x).

Here,Axmeanszxfor allz∈A, and similarly y≺Ameansy≺zfor allz∈A. Thus, /0xandy≺/0 for allxandy.

An important special case is summarized in the fol- lowing corollary:

Corollary 8. Let(F,F)be a p-box with0–1-valued F, and let B={x∈Ω^∗:F(x) =0}. IfΩ/'is order com- plete, then, for any A⊆Ω,

E_F,F(A) =min

y∈B^cF(supA∩[0_Ω,y]).

If, in addition, B^chas a minimum, then E_F,F(A) =F(supA∩[0_Ω,minB^c]).

If, in addition, F=I_[1

Ω]', then

E_F,F(A) =F(supA). (5) Note that Eq. (5) is essentially due to [7, paragraph preceeding Theorem 11]—they work with chains and multivalued mappings, whereas we work with total pre- orders. The case of order completeΩ/'is applicable for instance whenΩ is a finite space, or when it is an interval of real numbers.

In case of 0–1-valuedF, we arrive at the following expression:

Proposition 9. Let(F,F)be a p-box with0–1-valued F, and let C={x∈Ω^∗:F(x) =0}. Then, for any A⊆Ω,

E_F,F(A) =1−max

x∈C sup

y∈Ω^∗:y≺A∩(x,1_Ω]

F(y).

Again, we summarize an important special case in the following corollary:

Corollary 10. Let(F,F)be a p-box with0–1-valued F, and let C={x∈Ω^∗: F(x) =0}. IfΩ/'is order complete, and C has a maximum, then, for any A⊆Ω,

E_F,F(A) =











1−F(infA∩C^c) if A∩C^chas no minimum 1−F((minA∩C^c)−) if A∩C^chas a minimum.

If, in addition, F=1, then E_F,F(A) =

(1−F(infA) if A has no minimum 1−F(minA−) if A has a minimum.

4. P-Boxes as Possibility Measures.

In this section, we identify when p-boxes coincide ex- actly with a possibility measure. All results in this sec- tion rely on the following trivial, yet important, lemma:

Lemma 11. For a p-box (F,F) there is a possibility measureΠsuch that E_F,F=Πif and only if

E_F,F(A) =sup

x∈A

E_F,F({x})for all A⊆Ω

and in such a case,π(x) =E_F,F({x}).

We say thata p-box(F,F)is a possibility measure when the conditions of Lemma 11 are satisfied.

Taking into account that every possibility measure is maxitive, we deduce from Theorem 6 that, for a p-box to be a possibility measure, it is necessary that at least one ofF orF is 0–1-valued. The next corollary char- acterizes the additional regularity conditions to arrive at necessity and sufficiency:

Corollary 12. Assume thatΩ/'is order complete and let(F,F)be a p-box. Then(F,F)is a possibility mea- sure if and only if either

(L1) F is0–1-valued,

(L2) F(x) =F(x−)for all x∈Ωthat have no immediate predecessor, and

(L3) {x∈Ω^∗:F(x) =1}has a minimum, or

(U1) F is0–1-valued,

(U2) F(x) =F(x+)for all x∈Ωthat have no immediate successor, and

(U3) {x∈Ω^∗:F(x) =0}has a maximum.

Note that, in caseF=I_[1

Ω]', condition (L2) is essen- tially due to [7, Observation 9]. Also note that, forE_F,F to be a possibility measure, the conditions are still nec- essary even whenΩ/'is not order complete: the proof in this direction does not require order completeness.

As a special case, whenΩ/'is finite,E_F,Fis a pos- sibility measure with possibility distribution

π(x) =

(F(x) ifxmin{y:F(y) =1}

0 otherwise.

whenFis 0–1-valued, and π(x) =

(1−F(x−) ifF(x) =1

0 otherwise.

whenFis 0–1-valued.

We now characterise when, conversely, possibility measures can be represented as p-boxes. We shall see that this is possible for almost all of them.

5. From Possibility Measures to P-Boxes 5.1. Possibility Measures as Specific P-Boxes [11] already discuss the link between possibility mea- sures and p-boxes defined on the real line with the usual ordering. They show that any possibility measure can be approximated by a p-box, however at the expense of los- ing some information. We substantially strengthen their result, and even reverse it: we prove that any possibility measure with compact range can beexactlyrepresented by a p-box with vacuous lower cumulative distribution function, that is, withF=I_[1

Ω]'. In other words, gener- ally speaking, possibility measures are a special case of p-boxes on totally preordered spaces.

Theorem 13. For every possibility measureΠonΩwith possibility distributionπ such thatπ(Ω) ={π(x):x∈ Ω}is compact, there is a preorderonΩand an upper cumulative distribution function F such that the p-box (F=I_[1

Ω]',F)is a possibility measure with possibility distributionπ. In fact, one may take the preorderto be the one induced byπ(so xy wheneverπ(x)≤π(y)) and F=π.

The representing p-box is not necessarily unique:

Example14. LetΩ={x1,x₂}and letΠbe the possibil- ity measure determined by the possibility distribution

π(x₁) =0.5 π(x₂) =1.

By Theorem 13, this possibility measure can be obtained if we consider the orderx₁≺x₂and the p-box(F₁,F₁) given by

F₁(x₁) =0 F₁(x₂) =1 F₁(x₁) =0.5 F₁(x₂) =1.

However, we also obtain it if we consider the orderx2≺ x1and the p-box(F₂,F2)given by

F₂(x₁) =1 F₂(x₂) =0.5 F₂(x₁) =1 F₂(x₂) =1.

Indeed, by Corollary 12,E_F

2,F₂is a possibility measure.

By Eq. (4), its associated possibility distribution is E_F

2,F₂(x₂) =F(x₂)−F(x₂−) =1 E_F

2,F₂(x₁) =F(x₁)−F(x₁−) =0.5,

i.e., π, as with the given ordering, x2−=0_Ω− and

x1−=x2.

Moreover, a p-box may be maximum preserving even if neither the lower nor the upper distribution function is vacuous, as we have shown in Corollary 12.

Also, there are possibility measures which cannot be represented as p-boxes whenπ(Ω)is not compact:

Example15. LetΩ= [0,1], and consider the possibil- ity distribution given byπ(x) = (1+2x)/8 ifx<0.5, π(0.5) =0.4 andπ(x) =xifx>0.5; note thatπ(Ω) = [0.125,0.25)∪ {0.4} ∪(0.5,1]is not compact. The or- dering induced byπ is the usual ordering on[0,1]. Let Π be the possibility measure induced byπ. We show that there is no p-box(F,F)on([0,1],), regardless of the orderingon[0,1], such thatE_F,F=Π.

By Corollary 12, ifE_F,F=Π, then at least one ofF orF is 0-1–valued. Assume first thatF is 0-1–valued.

By Eq. (4),E_F,F({x}) =F(x)−F(x−) =π(x). Because π(x)>0 for allx, it must be thatF(x−) =0 for allx, so F=π. Because F is non-decreasing, xyif and only ifF(x)≤F(y); in other words,can only be the usual ordering on[0,1]for(F,F)to be a p-box. Hence, F=I_{1}.

For(F,F) to induce the possibility measure Π, we know from Corollary 12 that F(x) =F(x−)for every

xthat has no immediate predecessor, that is, for every x>0. But,F(0.5) =0.46=0.25=F(0.5−).

Similarly, ifF would be 0–1-valued, then we deduce from Eq. (4) thatF(x) =1 for everyx, again because π(x)>0 for allx. Therefore,F(x−) =1−π(x)for allx.

But, becauseF is non-decreasing, one can easily show thatcan only be the inverse of the usual ordering on [0,1]for(F,F)to be a p-box.

Now, for(F,F)to induce the possibility measureΠ, we know from Corollary 12 thatF(x) =F(x+)for every xthat has no immediate successor in with respect to, that is, for everyx≺0, or equivalently, for everyx>0.

Whence,

F(x) =F(x+) =inf

yxF(y−) =1−sup

y<x

π(y)

for allx>0. This leads to a contradiction: by the defi- nition ofπ, we have on the one hand,

F(0.5−) = sup

x>0.5

(1−sup

y<x

π(y)) =0.5 and on the other hand,

F(0.5−) =1−π(0.5) =0.6.

Hence,E_F,Fcoincides withΠin neither case.

Another way of relating possibility measures and p- boxes goes via random sets (see for instance [10] and [12]).

5.2. P-boxes as Conjunction of Possibility Measures In [12], where p-boxes are studied on finite spaces, it is shown that a p-box can be interpreted as the conjunction of two possibility measures, in the sense thatM(P_F,F) is the intersection of two sets of additive probabilities induced by two possibility measures. The next propo- sition extends this result to arbitrary totally preordered spaces.

Proposition 16. Let(F,F)be a p-box such that(F,I_Ω) and (I_[1

Ω]',F) are possibility measures (see Corol- lary 12). Then,(F,F)is the intersection of two pos- sibility measures defined by the distributions

π_F(x) =F(x)and π_F(x) =1−F(x−), in the sense thatM(P_F,F) =M(Π_π

F)∩M(Π_πF).

This suggests a simple way (already mentioned in [12]) to conservatively approximateE_F,Fby using the two possibility distributions: it holds that

max{E_π

F(A),E_πF(A)} ≤E_F,F(A)

≤E_F,F(A)≤min{E_π

F(A),E_πF(A)}.

This approximation is computationally attractive, as it allows us to use the supremum preserving properties of possibility measures. However, as next example shows, the approximation will usually be very conservative, and hence not likely to be helpful.

Example17. Considerx≺y∈Ω. The distance between E_F,Fand its approximation min{E_π

F,E_πF}on the inter- val(x,y]is given by

min{E_π

F((x,y]),E_πF((x,y])} −E((x,y])

=min{F(y),1−F(x)} −(F(y)−F(x))

=min{F(x),1−F(y)}.

Therefore, the approximation will be close to the exact value only when eitherF(x)is close to zero orF(y)is

close to one.

In general, the setM(Π₁)∩M(Π₂)associated with two possibility distributionsπ1andπ2is not a p-box. In- deed, it may be empty (take for instanceΩ={1,2}and the possibility measures determined by the distributions π1(1) =1,π1(2) =0.25,π₂(1) =0.25,π2(2) =1), or re- sult in another uncertainty model, such as a cloud [20].

In particular, whenM(Π₁)∩M(Π₂)is non-empty, the resulting coherent upper probability is in general not a possibility measure. An interesting open problem is then to characterise under which conditions some of the properties (i.e., maxitivity, complete monotonicity) of Π₁andΠ₂still hold for the coherent upper probability determined byM(Π₁)∩M(Π₂).

6. Natural Extension of0–1-Valued P-Boxes

From Proposition 7, we can derive an expression for the natural extension of a 0–1-valued p-box:

Proposition 18. Let (F,F) be a p-box where F = IC^c,F=IB^cfor some C⊆B⊆Ω. Then for any A⊆Ω,

E_F,F(A) =







0 if there are x∈C and y∈B^cs.t.

A∩Cx,A∩B∩C^c=/0,y≺A∩B^c 1 otherwise.

Moreover, Corollary 12 allows us to determine when this p-box is a possibility measure:

Proposition 19. Assume thatΩ/'is order complete.

Let(F,F)be a p-box where F=I_C^c,F=I_B^c for some C⊆B⊆Ω. The following statements are equivalent:

1. (F,F)is a possibility measure.

2. B^chas a minimum and C has a maximum.

In particular, we can characterise under which condi- tions a precise p-box, i.e., one whereF=F :=F, in- duces a possibility measure. The natural extension of precise p-boxes on the unit interval was considered in [21, Section 3.1]. From Theorem 6, the natural exten- sion ofF can only be a possibility measure whenF is 0–1-valued. If we apply Propositions 18 and 19 with B=Cwe obtain the following:

Corollary 20. Let(F,F)be a precise p-box where F= F is0–1-valued, and let B={x∈Ω^∗:F(x) =0}. Then, for every subset A ofΩ,

E_F,F(A) =







0 if there are x∈B,y∈B^c s.t. A∩Bx and y≺A∩B^c 1 otherwise.

If moreover Ω/'is order complete, then (F,F)is a possibility measure if and only if B has a maximum and B^chas a minimum.

As a consequence, we deduce that a precise 0–1- valued p-box never induces a possibility measure in case (Ω,) = ([0,1],≤), except ifF=I_[0,1].

7. Constructing Multivariate Possibility Measures from Marginals

In [13], multivariate p-boxes were constructed from marginals. We next apply this construction together with the p-box representation of possibility measures, given by Theorem 13, to build a joint possibility measure from some given marginals. As particular cases, we consider the joint,

(i) either without any assumptions about dependence or independence between variables, that is, using the Fréchet-Hoeffding bounds [22],

(ii) or assuming epistemic independence between all variables, which allows us to use the factorization property [23].

Let us considern variablesX₁, . . . , X_n, and assume that for each variableX_iwe are given a possibility mea- sureΠ_iwith corresponding possibility distributionπ_ion Xi. We assume that the range of all marginal possibil- ity distributions is[0,1]; in particular, Theorem 13 ap- plies, and each marginal can be represented by a p-box on(Xi,_i), with vacuousF_i, andF_i=π_i. Remember that the preorder_iis the one induced byπi.

7.1. Multivariate Possibility Measures

The construction in [13, Sect. 7] employs the following mappingZ, which induces a preorderonΩ=X1×

· · · ×Xn:

Z(x₁, . . . ,xn) =maxⁿ

i=1 πi(x_i).

With this choice ofZ, we can easily find the possibility measure which represents the joint as accurately as pos- sible, under any rule of combination of coherent lower probabilities:

Lemma 21. Letbe any rule of combination of coher- ent upper probabilities, mapping the marginals P₁, . . . , P_nto a joint coherent upper probability^Jn_i=1P_ion all events. If there is a continuous function u for which

n K

i=1

P_i

n

∏

A_i

!

=u(P₁(A₁), . . . ,P_n(A_n))

for all A₁⊆X1, . . . , A_n⊆Xn, then the possibility dis- tributionπdefined by

π(x) =u(Z(x), . . . ,Z(x))

induces the least conservative upper cumulative distri- bution function on(Ω,)that dominates the combina- tion^Jn_i=1ΠiofΠ1, . . . ,Πn.

This result is a consequence of [13, Lemma 22], which gives the least conservative p-box that domi- nates the natural extension of the combination of some marginal p-boxes by. Here we are considering the par- ticular case where the marginal p-boxes represent possi- bility measures, and approximating of the corresponding p-box by a possibility measure.

7.2. Natural Extension: The Fréchet Case

Thenatural extensionⁿ_i=1P_iofP₁, . . . ,P_nis the upper envelope of all joint (finitely additive) probability mea- sures whose marginal distributions are compatible with the given marginal upper probabilities. So, the model is completely vacuous (that is, it makes no assumptions) about the dependence structure, as it includes all possi- ble forms of dependence. See [24, p. 120, §3.1] for a rigorous definition. In this paper, we only need the fol- lowing equality, which is one of the Fréchet bounds (see for instance [14, p. 122, §3.1.1]):

ⁿi=1P_i

n

∏

A_i

!

=minⁿ

i=1P_i(A_i) for allA₁⊆X1, . . . ,A_n⊆Xn.

Theorem 22. The possibility distribution π(x) =maxⁿ

i=1π_i(x_i) (6)

induces the least conservative upper cumulative distri- bution function on(Ω,)that dominates the natural ex- tensionⁿi=1Π_iofΠ₁, . . . ,Π_n.

In other words, if we consider the marginal credal sets M(Π₁), . . . ,M(Π_n)and consider the setM of all the finitely additive probabilities onΩwhoseXi-marginals belong toM(Π_i)fori=1, . . . ,n, thenM is included in the credal set of the possibility distributionπas defined in Eq. (6).

Since it is based on very mild assumptions, it is not surprising that the possibility distribution given by Eq. (6) is very uninformative (that is, very close to a vacuous model whereπ(x) =1 for everyx): we shall haveπ(x) =1 as soon asπi(x_i) =1 for somei, even if πj(x_j) =0 for every j6=i. In particular, if one of the marginal possibility distributions is vacuous, then so is π. This also shows that the corresponding possibility measureΠmay not haveΠ1, . . . ,Πnas its marginals.

7.3. Independent Natural Extension

In contrast, we can consider joint models which satisfy the property ofepistemic independencebetween the dif- ferentX₁, . . . ,X_n. This property means, roughly speak- ing, that the conditional models that we can derive from the joint coincide with the marginals. The most conser- vative of these models is called theindependent natural extension⊗ⁿ_i=1P_iofP₁, . . . ,P_n. See [23] for a rigorous definition and properties, and [15] for a study of joint possibility measures that satisfy epistemic independence in the case of two variables. In this paper, we only need

the following equality for the independent natural exten- sion:

n O

i=1

P_i

n

∏

i=1

A_i

!

=

n

∏

i=1

P_i(A_i) (7) for allA₁⊆X1, . . . ,A_n⊆Xn.

Theorem 23. The possibility distribution π(x) =

n

max

i=1 πi(x_i) n

(8) induces the least conservative upper cumulative distri- bution function on(Ω,)that dominates the indepen- dent natural extension⊗ⁿ_i=1ΠiofΠ1, . . . ,Πn.

Note that the possibility measure determined Eq. (8) is only an outer approximation of the independent natu- ral extension: as shown in [15, Sec. 6], there is no least conservative possibility measure that corresponds to the independent natural extension of possibility measures.

One interesting consequence of Theorem 23 is that we can also use the possibility distribution determined by Eq. (8) under some other independence conditions for imprecise probabilities, such asstrongorKuznetsov independence [25, 26]. This is because the joint model they determine also satisfies the factorization condition given by Eq. (7), and as a consequence the least conser- vative upper cumulative distribution that dominates the strong or the Kuznetsov product of the marginal possi- bility measures is also determined by Eq. (8). See [23]

for more information on the relationship between factor- ization and independence.

We do not consider the minimum rule and the product rule

n

mini=1π_i(x_i)and

n

∏

i=1

π_i(x_i),

as their relation with the theory of coherent lower pre- visions is still unclear. However, we can compare the above approximation with the following outer approxi- mation given by [27, Proposition 1]:

π(x) =minⁿ

i=1(1−(1−πi(x_i))ⁿ). (9) The above equation is an outer approximation in case of random set independence, which is slightly more con- servative than the independent natural extension [28, Sec. 4], so in particular, it is also an outer approximation of the independent natural extension. Essentially, each distributionπi is transformed into 1−(1−πi)ⁿbefore applying the minimum rule. It can be expressed more simply as

1−maxⁿ

i=1(1−π_i(x_i))ⁿ.

If for instance π(x_i) =1 for at least one i, then this formula provides a more informative (i.e., lower) upper bound than Theorem 23. On the other hand, when all π(x_i)are, say, less than 1/2, then Theorem 23 does bet- ter.

Finally, note that neither Eq. (8) nor Eq. (9) are proper joints, in the sense that, in both cases, the marginals

of the joint are outer approximations of the original marginals, and will in general not coincide with the orig- inal marginals.

8. Conclusions

Both possibility measures and p-boxes can be seen as coherent upper probabilities. We used this framework to study the relationship between possibility measures and p-boxes. Following [13], we allowed p-boxes to be defined on arbitrary totally preordered spaces, whence including p-boxes on finite spaces, on real intervals, and even multivariate ones.

We began by considering the more general case of maxitive measures, and proved that a necessary and sufficient condition for a p-box to be maxitive is that at least one of the cumulative distribution functions of the p-box must be 0–1 valued. Moreover, we deter- mined the natural extension of a p-box in those cases and gave a necessary and sufficient condition for the p-box to be supremum-preserving, i.e., a possibility measure.

As special cases, we also studied p-boxes where both the lower and the upper distribution functions are 0–1–

valued, and in particular precise 0–1 valued p-boxes.

Secondly, we showed that almost every possibility measure can be represented as a p-box. Hence, in gen- eral, p-boxes are more expressive than possibility mea- sures, while still keeping a relatively simple representa- tion and calculus [13], unlike many other models, such as for instance lower previsions and credal sets, which typically require far more advanced techniques, such as linear programming.

Finally, we considered the multivariate case in more detail, by deriving a joint possibility measure from given marginals using the p-box representation established in this paper and results from [13].

In conclusion, we established new connections be- tween both models, strengthening known results from literature, and allowing many results from possibility theory to be embedded into the theory of p-boxes, and vice versa.

As future lines of research, we point out the generali- sation of a number of properties of possibility measures to p-boxes, such as the connection with fuzzy logic [1]

or the representation by means of graphical structures [29], and the study of the connection of p-boxes with other uncertainty models, such as clouds and random sets.

Acknowledgements

Work partially supported by projects TIN2008-06796- C04-01 and MTM2010-17844 and by a doctoral grant from the IRSN.

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