The Framework of the Paradigm of Complex Probability and Monté Carlo Methods | Book Publisher International

 

Calculating probabilities is a crucial task of classical probability theory. Adding supplementary dimensions to nondeterministic experiments will yield a deterministic expression of the theory of probability. This is the novel and original idea at the foundation of my complex probability paradigm. As a matter of fact, probability theory is a stochastic system of axioms in its essence; that means that the phenomena outputs are due to randomness and chance. By adding novel imaginary dimensions to the non-deterministic phenomenon happening in the set R will lead to a deterministic phenomenon and thus a stochastic experiment will have a certain output in the complex probability set C. If the chaotic experiment becomes completely predictable then we will be fully capable of predicting the output of random events that occur in the real world in all stochastic processes. Accordingly, the task that has been achieved here was to extend the random real probabilities set R to the deterministic complex probabilities set C = R + M and this by incorporating the contributions of the set M which is the associated and complementary imaginary set of probabilities to the set R. Hence, the probability in C is computed after the subtraction of the chaotic factor from the degree of our knowledge of the nondeterministic experiment. Consequently, since this extension was revealed to be successful, then an innovative paradigm of stochastic sciences and prognostic was put forward in which all nondeterministic phenomena in R were expressed deterministically in C. I coined this novel model with the term "The Complex Probability Paradigm (or CPP)" which was initiated and established in my earlier research works. Moreover, this pioneering paradigm will be applied in a creative manner to the stochastic procedures and algorithms of the famous and historical Buffon’s needle method to compute PI, to the renowned neutron shielding problem, and to numerous and various topics that arise in Monté Carlo Methods.

 

Author(s) Details

Abdo Abou Jaoudé

Department of Mathematics and Statistics, Faculty of Natural and Applied Sciences, Notre Dame University-Louaizé, Lebanon.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-48006-59-2

The Paradigm of Complex Probability and Absorbing Markov Chains | Chapter 4 | The Paradigm of Complex Probability and Markov Chains, Edition 1

 

The five fundamental axioms of classical probability theory were put forward in 1933 by Andrey Nikolaevich Kolmogorov. Encompassing new imaginary dimensions with the experiment real dimensions will make the work in the complex probability set totally predictable and with a probability permanently equal to one. This is the original idea in my complex probability paradigm. Therefore, this will make the event in C = R + M absolutely deterministic by adding to the real set of probabilities R the contributions of the imaginary set of probabilities M. It is of great importance that stochastic systems become totally predictable since we will be perfectly knowledgeable to foretell the outcome of all random events that occur in nature. Consequently, by calculating the parameters of the new prognostic model, we will be able to determine the magnitude of the chaotic factor, the degree of our knowledge, the real and imaginary and complex probabilities in the probability sets R and M and C and which are all subject to chaos and random effects. Hence, we will apply this novel paradigm to Absorbing Markov Chains Theory in order to express it totally and absolutely deterministically in the complex universe C of probabilities.

 

Author(s) Details

Abdo Abou Jaoudé
Department of Mathematics and Statistics, Faculty of Natural and Applied Sciences, Notre Dame University-Louaizé, Lebanon.

 

Please see the book here: https://doi.org/10.9734/bpi/mono/978-93-48006-18-9/CH4