# Some Neutrosophic Probability Distributions.

1 Introduction: Neutrosophy theory introduced by Smarandache in 1995. It is a new branch of philosophy, presented as a generalization for the fuzzy logic [5] and as a generalization for the intuitionistic fuzzy logic [6]. The fundamental concepts of neutrosophic set, introduced by Smarandache in [7, 8, 9, 10], and Salama et al. in [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], provides a new foundation for dealing with issues that have indeterminate data. The indeterminate data may be numbers, and the neutrosophic numbers have been defined in [24, 25, 26, 27]. In this paper, we highlight the use of neutrosophic crisp sets theory [3,4] with the classical probability distributions, particularly Poisson distribution, Exponential distribution and Uniform distribution, which opens the way for dealing with issues that follow the classical distributions and at the same time contain data not specified accurately. The extension of classical distributions according to the neutrosophic logic, means that parameters of classical distribution take undetermined values, which allows dealing with all the situations that one may encounter while working with statistical data and especially when working with vague and inaccurate statistical data, Florentin Smarandache presented the neutrosophic binomial distribution and the neutrosophic natural distribution [1,2] in 2014, In this paper, we will discuss continuous random distributions such as the Exponential distribution and Uniform distribution, and discontinuous random distribution such as Poisson distribution by using neutrosophic logic.2 TERMINOLOGIES: We recollect some relevant basic preliminaries, and in particular, the work of Smarandache in [1, 9, 10], and Salama et al. [22, 23]. We consider the following classic statistical distributions Poisson distribution, Exponential distribution and Uniform distribution.

3 Neutrosophic probability Distributions:

3.1 Neutrosophic Poisson Distribution:

3.1.a Definition: Neutrosophic Poisson distribution of a discrete variable X is a classical Poisson distribution of X, but its parameter is imprecise. For example, I can be set with two or more elements. The most common such distribution is when I is interval.

[mathematical expression not reproducible]

[[lambda].sub.N]: Is the distribution parameater.

* [[lambda].sub.N]: is equal to the expected value and the variance.

NE(x) = NV(x) = [[lambda].sub.N]

Where, N = d + I is a neutrosophic statistical number in [2].

3.1.b Example for Case study:

In a company, Phone employee receives phone calls, the calls arrive with rate of [1, 3] calls per minute, we will calculate the probability that:

--The employee will not receive any call within a minute:

Assuming x: the number of calls in a minute.

Then:

[mathematical expression not reproducible]

For [lambda] = 1:

NP(0) = [e.sup.-1] = 0.3679

For [lambda] = 3:

NP( 0) = [e.sup.-3] = 0.0498

Thus, the probability that employee won't receive any call, within a minute, ranges between [0.0498, 0.3697].

--the probability that employee won't receive any call, within 5 minutes:

Then:

[[lambda].sub.N] = 5. [1,3] = [5,15]

NP(x) = [e.sup.-[5,15]], [([5,15]).sup.X]/x!; x = 0, 1, ...

[mathematical expression not reproducible]

For [lambda] = 5:

NP(0) = [e.sup.-5] = 0.0067

For [lambda] = 15:

NP(0) = [e.sup.-15] = 0.000000306

Thus, the probability that the employee will not receive any call within 5 minutes ranges between [0.000000306, 0.0067].

3.2 The Neutrosophic Exponential Distribution:

3.2.a Definition: Neutrosophic exponential distribution [21] is defined as a generalization of classical exponential distribution, Neutrosophic exponential distribution can deals with all the data even non-specific, we express the density function as:

[mathematical expression not reproducible]

exp([[lambda].sub.K]): Neutrosophic Exponential Distribution.

[X.sub.N]: X neutrosophic random variable [22].

[[lambda].sub.N]: distribution parameater.

3.2.b the distribution properties:

1--Expected value:

E(x) = 1/[[lambda].sub.N]

Variance:

[mathematical expression not reproducible]

2--Distribution function:

Probability to terminate the client's service in less than a minute:

[mathematical expression not reproducible]

3.2.c. Example for Case study:

The time required to terminate client's service in the bank follows an exponential distribution, with an average of one minute, let us write a density function that represents the time required for terminating client's service, and then calculate the probability of terminating client's service in less than a minute.

Solution:

--Assuming x: represents the time required for termination of the client's service per minute.

--The average 1/[lambda] = 1 [??] [lambda] = 1

Therefore, the Probability density function:

f(x) = [e.sup.-x]; 0 < X < [infinity]

--The possibility of client's service terminated in less than a minute:

p(X [less than or equal to] 1) = (1 - [e.sup.-x]) = (1 - [e.sup.-(1)]) = 0.63

--The above example is a simple example practically, but if it is changed to the following:

The time required to terminate client's service in the bank follow an exponential distribution, with an average of [0.67, 2] minute. We know that classical exponential distribution only deals with data defined accurately, note that the average here is an interval, how we will deal with this situation.

So, we will turn to the neutrosophic exponential distribution to solve this issue:

For exponential distribution, its average [0.67, 2] minutes, we write:

1/[[lambda].sub.N] = [0.67,2 ] [??] [[lambda].sub.N] = 1/[0.67, 2] = [0.5, 1.5]

The probability density function:

[mathematical expression not reproducible]

Probability to terminate the client's service in less than a minute:

[mathematical expression not reproducible]

We note:

For [lambda] = 0.5:

NP(X [less than or equal to] 1) = 1 - [e.sup.-05] = 0.39

For [lambda] = 1.5:

NP(X [less than or equal to] 1) = 1 - [e.sup.-1.5] = 0.78

That is, the probability of terminating client's service in less than a minute ranges between [0.39, 0.78].

--Note that, the value of the classic probability to terminate client's service in less than a minute is one of the domain values for the neutrosophic probability:

p(X [less than or equal to] 1) = 0.63 [member of] [0.39,0.78] = NP(X [less than or equal to] 1)

And the solutions are the shaded area in Figure 1.

3.2.d Note: We also mention the relationship of exponential distribution with Poisson distribution, If the occurrence of events follows the Poisson distribution, the duration between the occurrence of two events follow exponential distribution. For example, arrival of customers to a service centre follows the Poisson distribution, the time between the arrival of a customer and the next customer follow the exponential distribution. Thus, when the parameter X is inaccurately defined, we are dealing with the neutrosophic exponential distribution and the neutrosophic Poisson distribution, and we write:

If an event is repeated in time according to the neutrosophic Poisson distribution:

[mathematical expression not reproducible]

Then, the time between two events follows the neutrosophic exponential distribution:

[mathematical expression not reproducible]

3.2.d.i. Example:

Assuming that we have a machine in a factory. The rate of machine breakdowns is [1, 2] per week, let's calculate the possibility of no breakdowns per week, and calculate the possibility that at least two weeks pass before the appearance of the following breakdowns.

Solution:

--The possibility of no breakdowns in the week:

Assume x: variable represents occurrence of breakdowns in the week.

We note that, x is a variable that is subject to the neutrosophic Poisson distribution, the distribution parameter is

[[lambda].sub.N] = [1,2], thus:

[mathematical expression not reproducible]

Then, the possibility of no breakdowns in the week ranges between [0.135, 0.368].

--Assuming y: is represent the time before the appearance of the following breakdowns, we note that y is a variable following the neutrosophic exponential distribution, then:

[mathematical expression not reproducible]

Thus, the possibility that at least two weeks pass before the appearance of the following breakdowns, ranges between [0.018, 0.135].

3.3 Neutrosophic Uniform Distribution:

3.3.a Definition: Neutrosophic Uniform distribution of a continuous variable X, is a classical Uniform distribution, but distribution parameters a or b or both are imprecise. For example, a or b or both are sets with two or more elements (may a or b or both are intervals) with a < b.

3.3.b Example for Case study:

Assuming x is a variable represents a person's waiting time to passengers' bus (in minutes), bus's arrival time is not specified, the station official said:

1--the bus arrival time is: either from now to 5 minutes [0,5] or will arrive after 15 to 20 minutes[15,20], then:

a=[0, 5], b=[15, 20]

Then, the density function:

[f.sub.N] (x) = - 1/b - a = 1/[15.20] - [0.5] = 1/[10.20] = [0.05, 0.1]

The solution in the Graph is the shaded area, with the probability to moving (a) between [0, 5] and (b) between [15, 20].

2--The bus arrives after five minutes or will arrive after 15 to 20 minutes [15, 20], then:

a=5 b=[15, 20]

Then, the density function:

[f.sub.N](x) = 1/b - a = 1/[15,20] - 5 = 1/[10,15] = [0.067, 0.1]

The solution is the shaded area, with the probability to moving (b) between [15, 20].

--There are many non-specific situations that we encounter about the values a, b such as a, b or both are intervals (a, b or both are sets with two or more elements), we deal with these situations as the cases studied above.

4 The research gap

The classical probability distributions only deal with the specified data. The classical distribution parameters are always given with a specified value. This paper contributes to the study of classical distributions with undetermined values, and distribution parameters such as periods. We call these distributions neutrosophic probability distributions.

Conclusion:

We conclude from this paper that the neutrosophic probability distributions gives us a more general and clarity study of the studied issue, So that the classical probability is one solution among the solutions resulting from the study, of course, this is produced by giving the distribution parameters several options possible and does not remain linked to a single value. This paper is to present some the neutrosophic probability distributions, and we present various solved for the problems that classic logic is not deal with it. We look forward in the future to study other types of probability distributions according to the neutrosophic logic, especially the gamma distribution and student distribution and other distributions that have not yet been studied.

References

[1] Smarandache. F, Neutrosophical statistics. Sitech & Education publishing, 2014.

[2] Patro. S.K, Smarandache. F. the Neutrosophic Statistics Distribution, More Problems, More Solutions. Neutrosophic Sets and Systems, Vol. 12, 2016.

[3] Salama. A.A, Smarandache. F, Neutrosophic Crisp Set Theory. Education Publishing, Columbus, 2015.

[4] Salama. A.A, Smarandache. F, and Kroumov. V, Neutrosophic Crisp Sets & Neutrosophic Crisp Topological Spaces. Neutrosophic Sets and Systems, Vol. 2, pp. 25-30, 2014.

[5] Zadeh. L, Fuzzy Sets. Inform. Control 8, (1965).

[6] Atanassov.k, Intuitionistic fuzzy sets. In V. Sgurev, ed., ITKRS Session, Sofia, June 1983, Central Sci. and Techn. Library, Bulg. Academy of Sciences, 1984.

[7] Smarandache, F, Neutrosophy and Neutrosophic Logic, First International Conference on Neutrosophy, Neutrosophic Logic, Set, Probability, and Statistics University of New Mexico, Gallup, NM 87301, USA, 2002.

[8] Smarandache, F. A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability. American Research Press, Rehoboth, NM, 1999.

[9] Smarandache, F, Neutrosophic set a generalization of the intuitionistic fuzzy sets. Inter. J. Pure Appl. Math., 24, 287-297, 2005.

[10] Smarandache. F, Introduction to Neutrosophic Measure, Integral, Probability. Sitech Education publisher, 2015.

[11] Hanafy, I. M, Salama, A. A and Mahfouz, K, Correlation of Neutrosophic Data, International Refereed Journal of Engineering and Science (IRJES), 1(2), PP39-33, 2012.

[12] Hanafy, I. M., Salama, A. A, and Mahfouz, K. M, Neutrosophic Classical Events and Its Probability. International Journal of Mathematics and Computer Applications Research (IJMCAR), 3(1), 171-178, 2013.

[13] Salama, A. A, and Alblowi, S. A, Generalized Neutrosophic Set and Generalized Neutrosophic Spaces. Journal Computer Sci. Engineering, 2 (7), 129-132, 2012.

[14] Salama, A. A, and Alblowi, S. A, Neutrosophic Set and Neutrosophic Topological Spaces, ISOR J. Mathematics, 3(3), 31-35,2012.

[15] Salama, A. A, Neutrosophic Crisp Point & Neutrosophic Crisp Ideals. Neutrosophic Sets and Systems, 1(1), 50-54, 2013.

[16] Salama, A. A, and Smarandache, F, Filters via Neutrosophic Crisp Sets. Neutrosophic Sets and Systems, 1(1), 34-38, 2013.

[17] Salama, A. A, and Alblowi, S. A, Intuitionistic Fuzzy Ideals Spaces. Advances in Fuzzy Mathematics, 7(1), 51- 60, 2012.

[18] Salama, A. A, and Elagamy, H, Neutrosophic Filters. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), 3(1), 307-312, 2013.

[19] Salama, A. A, Smarandache, F, and Kroumov, V, Neutrosophic crisp Sets & Neutrosophic crisp Topological Spaces. Sets and Systems, 2(1),25-30,2014.

[20] Salama, A. A, Smarandache. F, and ALblowi, S. A, New Neutrosophic Crisp Topological Concepts. Neutrosophic Sets and Systems, 2014.

[21] Alhabib.R, Ranna.M, Farah.H and Salama, A. A, Neutrosophic Exponential Distribution. Albaath University Journal, Vol. 40, 2018. (Arabic version).

[22] Alhabib. R, Ranna. M, Farah. H and Salama, A. A, studying the random variables according to Neutrosophic logic. Albaath-university Journal, Vol (39), 3, pp129-151, 2018. (Arabic version).

[23] Alhabib.R, Ranna.M, Farah.H and Salama, A. A, "Foundation of Neutrosophic Crisp Probability Theory", Neutrosophic Operational Research, Volume III, Edited by Florentin Smarandache, Mohamed Abdel-Basset and Dr. Victor Chang (Editors), pp. 49-60, 2018.

[24] Mondal, K., Pramanik, S., & Smarandache, F. NN-harmonic mean aggregation operators-based MCGDM strategy in a neutrosophic number environment. Axioms 2018, 7, 12; doi: 10.3390/axioms7010012.

[25] Pramanik, S., & Dey, P.P. (2018). Bi-level linear programming problem with neutrosophic numbers. Neutrosophic Sets and Systems, 21, 110-121. https://doi. org/10.5281/zenodo.1408669

[26] Pramanik, S., Banerjee, D. (2018). Neutrosophic number goal programming for multi-objective linear programming problem in neutrosophic number environment. MOJ Current Research & Review, 1(3), 135-141. doi:10.15406/mojcrr.2018.01.00021

[27] Banerjee, D. Pramanik, S (2018). Single-objective linear goal programming problem with neutrosophic numbers. International Journal of Engineering Science & Research Technology, 7(5), 454-469.

[28] Smarandache, F. & Pramanik, S. (Eds). (2018). New trends in neutrosophic theory and applications, Vol. 2. Brussels: Pons Editions.

[29] Smarandache, F. & Pramanik, S. (Eds). (2016). New trends in neutrosophic theory and applications. Brussels: Pons Editions.

[30] Broumi, S., Bakali, A., Talea, M., Smarandache, F., Ulucay, V., Sahin, S., Dey, A., Dhar, M., Tan, R. P., de Oliveira, A., & Pramanik, S. (2018). Neutrosophic sets: An overview. In F. Smarandache, & S. Pramanik (Eds., vol. 2), New trends in neutrosophic theory and applications (pp. 403-434). Brussels: Pons Editions.

[31] Pramanik, S., Dalapati, S., Alam, S., Smarandache, S., & Roy, T.K. (2018). NS-cross entropy based MAGDM under single valued neutrosophic set environment. Information, 9(2), 37; doi:10.3390/info9020037.

[32] Dalapati, S., Pramanik, S., Alam, S., Smarandache, S., & Roy, T.K. (2017). IN-cross entropy based magdm strategy under interval neutrosophic set environment. Neutrosophic Sets and Systems, 18, 43-57. http://doi.org/10.5281/zenodo.1175162

[33] Biswas, P., Pramanik, S., & Giri, B. C. (2018). Distance measure based MADM strategy with interval trapezoidal neutrosophic numbers. Neutrosophic Sets and Systems, 19, 40-46.

[34] Biswas, P., Pramanik, S., & Giri, B. C. (2018). TOPSIS strategy for multi-attribute decision making with trapezoidal numbers. Neutrosophic Sets and Systems,19, 29-39.

[35] Biswas, P., Pramanik, S., & Giri, B. C. (2018). Multi-attribute group decision making based on expected value of neutrosophic trapezoidal numbers. In F. Smarandache, & S. Pramanik (Eds., vol. 2), New trends in neutrosophic theory and applications (pp. 103-124). Brussels: Pons Editions.

[36] Mondal, K., Pramanik, S., & Giri, B. C. (2018). Single valued neutrosophic hyperbolic sine similarity measure based MADM strategy. Neutrosophic Sets and Systems, 20, 3-11. http://doi.org/10.5281/zenodo.1235383

[37] Mondal, K., Pramanik, S., & Giri, B. C. (2018). Hybrid binary logarithm similarity measure for MAGDM problems under SVNS assessments. Neutrosophic Sets and Systems, 20, 12-25. http://doi.org/10.5281/zenodo.1235365

[38] Biswas, P, Pramanik, S. & Giri, B.C. (2016). Aggregation of triangular fuzzy neutrosophic set information and its application to multi-attribute decision making. Neutrosophic Sets and Systems, 12, 20-40.

[39] Biswas, P, Pramanik, S. & Giri, B.C. (2016). Value and ambiguity index based ranking method of single-valued trapezoidal neutrosophic numbers and its application to multi-attribute decision making. Neutrosophic Sets and Systems, 12, 127-138.

[40] Pramanik, S., Biswas, P., & Giri, B. C. (2017). Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment. Neural Computing and Applications, 28 (5), 1163-1176. DOI 10.1007/s00521-015-2125-3.

[41] Mondal, K., & Pramanik, S. (2015). Neutrosophic decision-making model for clay-brick selection in construction field based on grey relational analysis. Neutrosophic Sets and Systems, 9, 64-71.

[42] Mondal, K., & Pramanik, S. (2015). Neutrosophic tangent similarity measure and its application to multiple attribute decision-making. Neutrosophic Sets and Systems, 9, 85-92.

[43] P. Biswas, S. Pramanik, B.C. Giri. (2016). TOPSIS method for multi-attribute group decision making under single-valued neutrosophic environment. Neural Computing and Applications, 27(3), 727-737. doi: 10.1007/s00521-015-1891-2.

[44] Biswas, P., Pramanik, S., & Giri, B.C. (2015). Cosine similarity measure based multi-attribute decision-making with trapezoidal fuzzy neutrosophic numbers. Neutrosophic Sets and Systems, 8, 46-56.

[45] Biswas, P, Pramanik, S. & Giri, B.C. (2014). A new methodology for neutrosophic multi-attribute decision-making with unknown weight information. Neutrosophic Sets and Systems, 3, 42-50.

[46] Biswas, P, Pramanik, S. & Giri, B.C. (2014). Entropy based grey relational analysis method for multi-attribute decision making under single valued neutrosophic assessments. Neutrosophic Sets and Systems, 2, 102-110.

[47] Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). An Extension of Neutrosophic AHP-SWOT Analysis for Strategic Planning and Decision-Making. Symmetry, 10(4), 116.

[48] Abdel-Basset, M., Mohamed, M., Smarandache, F., & Chang, V. (2018). Neutrosophic Association Rule Mining Algorithm for Big Data Analysis. Symmetry, 10(4), 106.

[49] Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). A Hybrid Neutrosophic Group ANP-TOPSIS Framework for Supplier Selection Problems. Symmetry, 10(6), 226.

[50] Abdel-Basset, M., Gunasekaran, M., Mohamed, M., & Smarandache, F. (2018). A novel method for solving the fully neutrosophic linear programming problems. Neural Computing and Applications, 1-11.

[51] Abdel-Basset, M., Mohamed, M., & Chang, V. (2018). NMCDA: A framework for evaluating cloud-computing services. Future Generation Computer Systems, 86, 12-29.

[52] Abdel-Basset, M., Zhou, Y., Mohamed, M., & Chang, V. (2018). A group decision-making framework based on neutrosophic VIKOR approach for e-government website evaluation. Journal of Intelligent & Fuzzy Systems, 34(6), 4213-4224.

[53] Abdel-Basset, M., Mohamed, M., Zhou, Y., & Hezam, I. (2017). Multi-criteria group decision making based on neutrosophic analytic hierarchy process. Journal of Intelligent & Fuzzy Systems, 33(6), 4055-4066.

[54] Abdel-Basset, M., & Mohamed, M. (2018). The role of single valued neutrosophic sets and rough sets in smart city: imperfect and incomplete information systems. Measurement, 124, 47-55.

[55] Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems.

[56] Abdel-Basset, M., Gunasekaran, M., Mohamed, M., & Chilamkurti, N. (2018). Three-way decisions based on neutrosophic sets and AHP-QFD framework for supplier selection problem. Future Generation Computer Systems.

[57] Chang, V., Abdel-Basset, M., & Ramachandran, M. (2018). Towards a Reuse Strategic Decision Pattern Framework-from Theories to Practices. Information Systems Frontiers, 1-18.

[58] Abdel-Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2018). A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Design Automation for Embedded Systems, 1-22.

Received: September 6, 2018. Accepted: October 1, 2018

Rafif Alhabib (1), Moustafa Mzher Ranna (2), Haitham Farah (3), A.A. Salama (4)

(1,2) Department of Mathematical Statistics, Faculty of Science, Aleppo University, Syria, Rafif.alhabib85@gmail.com (3) Department of Mathematical Statistics, Faculty of Science, Albaath University, Syria (4) Department of Mathematics and computer science, Faculty of Science, Port said University, Egypt drsalama44@gmail.com

Caption: Figure 1

Caption: Figure 2

Caption: Figure 3

Printer friendly Cite/link Email Feedback | |

Author: | Alhabib, Rafif; Ranna, Moustafa Mzher; Farah, Haitham; Salama, A.A. |
---|---|

Publication: | Neutrosophic Sets and Systems |

Article Type: | Report |

Date: | Nov 1, 2018 |

Words: | 3293 |

Previous Article: | Optimization of EOQ Model with Limited Storage Capacity by Neutrosophic Geometric Programming. |

Next Article: | A framework for selecting cloud computing services based on consensus under single valued neutrosophic numbers. |

Topics: |