1. Introduction
In general, a conditional mean value has many applications in regression analysis and in financial mathematics and insurance. The most used notion in these areas is uncertainty. The notion of uncertainty has two aspects. The first one is understood as risk uncertainty and it is modeled by a stochastic apparatus. The second one is vagueness, which can be modeled by fuzzy methodology.
In [
1], A. de Korvin and R. Kleyle studied a conditional expectation in a fuzzy case and they showed its use for Gaussian distribution. A conditional variance of fuzzy random variables needs for its definition the notion of a conditional mean value (see [
2]). An approach to modeling risk by the conditional value at risk methodology under imprecise and soft conditions was solved in [
3]. In [
4], C. You discussed the properties of a conditional mean value for fuzzy variables such as the Hölders inequality. In [
5], M. Bertanha and G. W. Imbens showed the use of a conditional mean value for testing an external validity in fuzzy regression discontinuity designs. B. Riečan and M. Jurečková studied a notion of conditional expectation of observables on MV-algebras of fuzzy sets and on probability MV-algebras with product (see [
6,
7]). The intuitionistic fuzzy sets introduced by K. T. Atanassov in [
8] are a generalization of Zadeh’s fuzzy sets in [
9,
10], given by
, where
f is a fuzzy set. As there are known practical applications in classical cases and fuzzy cases, it is interesting to study a notion of conditional expectation in a family of intuitionistic fuzzy sets.
In [
11], V. Valenčáková defined a conditional mean value
for crisp intuitionistic fuzzy events
as a Borel function
satisfying the following equality
for every
and
. There,
,
are the M-observables and
is an M-state. She used the Gödel connectives ∨, ∧ given by
for any
.
In this paper, we define a conditional mean value for the family of intuitionistic fuzzy events
. We use the Lukasiewicz connectives
given by
for any
. We show the connection with a conditional intuitionistic probability
introduced by B. Riečan in [
12] as a Borel measurable function
f (i.e.,
) such that
for each
, where
is the intuitionistic fuzzy state,
is an intuitionistic fuzzy event and
is an intuitionistic fuzzy observable. This conditional intuitionistic fuzzy probability is induced by an intuitionistic fuzzy state.
The paper is organized as follows:
Section 2 includes the basic notions from intuitionistic fuzzy probability theory as an intuitionistic fuzzy event, an intuitionistic fuzzy state, an intuitionistic fuzzy observable and an intuitionistic fuzzy mean value. In
Section 3, we present the main results of the research. First, we formulate a definition of an indefinite integral. Then we define a conditional intuitionistic fuzzy mean value for the intuitionistic fuzzy events. Next, we show a connection with a conditional intuitionistic probability induced by an intuitionistic fuzzy state. The last section contains concluding remarks and the direction of future research.
We note that in the whole text we use the notation IF as an abbreviation for ‘intuitionistic fuzzy.’
2. Basic Notions of the Intuitionistic Fuzzy Probability Theory
In this section, we recall the definitions of basic notions connected with intuitionistic fuzzy probability theory (see [
13,
14,
15]).
Definition 1 ([13,14,15]).Let Ω be a nonempty set. An IF-set on Ω is a pair of mappings such that . Definition 2 ([13,14,15]).Start with a measurable space . Hence, is a σ-algebra of subsets of Ω. An -event is called an -set such that are -measurable. The family of all -events on will be denoted by , will be called the membership function, will be called the non-membership function.
If
,
, then we define the Lukasiewicz binary operations
on
by
and the partial ordering is given by
In the
-probability theory ([
12]), instead of the notion of probability, we use the notion of state.
Definition 3 ([12]).Let be the family of all IF-events in Ω. A mapping is called an IF-state, if the following conditions are satisfied: - (i)
, ;
- (ii)
if and , then ;
- (iii)
if (i.e., , ), then .
The third basic notion in the probability theory is the notion of an observable. Let
be the family of all intervals in
R of the form
Then the -algebra is denoted and it is called the -algebra of Borel sets and its elements are called Borel sets.
Definition 4 ([12]).By an IF-observable on we understand each mapping , satisfying the following conditions: - (i)
, ;
- (ii)
if , then and ;
- (iii)
if , then .
If we denote for each , then are observables, where .
Similar to the classical case, the following theorem can be proved ([
12,
16]).
Theorem 1 ([16]).Let be an IF-observable, be an IF-state. Define the mapping by the formulaThen is a probability measure. Proof. In [
16] Proposition 3.1. □
In [
17] we introduced the notion of product operation on the family of
-events
and showed an example of this operation.
Definition 5 ([17]).We say that a binary operation · on is a product if it satisfies the following conditions: - (i)
for each ;
- (ii)
the operation · is commutative and associative;
- (iii)
if and , thenandfor each ; - (iv)
if , and , then .
The following theorem provides an example of product operation for -events.
Theorem 2 ([17]).The operation · defined byfor each is a product operation on . Since now
plays an analogous role as
, we can define IF-mean value
by the same formula (see [
18]).
Definition 6 ([18]).We say that an IF-observable x is an integrable IF-observable, if the integral exists. In this case we define the IF-mean value If the integral exists, then we define IF-dispersion by the formula 3. Conditional Intuitionistic Fuzzy Mean Value
In this section, we present the main results. First, we introduce our motivation from classical probability space.
In the classical probability space
if
are two random variables, then the conditional mean value
of
with respect to
can be defined as a Borel function
such that
for each
. Here
is the probability distribution of
. In our case this idea could also be realised:
Of course, first we must define
, because we have defined
only.
Definition 7. If are the -observable and is fixed, then we define by the formula Proposition 1. The mapping is an IF-observable. If the IF-observable y is integrable, then the IF-observable is also integrable.
Proof. Let
,
. If
,
, then using Definitions 4 and 5 we have
If
,
, then
and using Definition 4 and Definition 5, we obtain
If , , then and we have , similar to the previous case.
Since
, then using Definition 5 we have
Let
y be integrable, that is, there exists
. We want to prove that
is integrable, too. It suffices to prove that there exist the integrals
Define the measure
by the formula
Then
is a measure and
It follows,
On the other hand, for
we have
□
Definition 8. If are the -observables, such that y is integrable, (i.e., there exists ), then the indefinite integral is defined by the formulafor fixed . Proposition 2. Let be the IF-observables and y be integrable. Then is a finite generalized measure.
Proof. Let
,
be disjoint. Then
. Put
Since
, then we have
□
Theorem 3. Let be the IF-observables and y be integrable, that is, there exists . Then there exists a Borel measurable function such thatfor each . Proof. Define by the formula and by the formula .
If
, that is,
, then
Hence,
Therefore
and by Radom-Nikodym theorem there exists the Borel measurable function
such that
for each
. □
Now we are able to define a notion of a conditional intuitionistic fuzzy mean value (expectation).
Definition 9. If are the -observables and y is integrable, then the conditional -mean value (expectation) is the Borel measurable function such thatfor each . Now we show the connection between a conditional intuitionistic fuzzy mean value
and a conditional intuitionistic probability
introduced by B. Riečan in [
12] (see
Remark 1). Recall that a conditional intuitionistic fuzzy probability is a Borel measurable function
f (i.e.,
) such that
for each
, where
is the intuitionistic fuzzy state,
is an intuitionistic fuzzy event and
is an intuitionistic fuzzy observable.
Remark 1. Take and define the -observable byThen holds - almost everywhere. Proof. Namely,
for each
.
Hence holds —almost everywhere. □