Percorrer por autor "Assis, Rui"

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    Application of Monte-Carlo Simulation towards a better understanding of Bayes´ Theorem in Engineering Education
    (Edições Universitárias Lusófonas, 2022) Assis, Rui; Marques, Pedro Carmona; Vidal, Raphaela; Faculdade de Engenharia
    Bayes' Theorem (BT) is treated in probability theory and statistics. The BT shows how to change the probabilities a priori in view of new evidence, to obtain probabilities a posteriori. With the Bayesian interpretation of probability, the BT is expressed as the probability of an event (or the degree of belief in the occurrence of an event) should be changed, after considering evidence about the occurrence of that event. Bayesian inference is fundamental to Bayesian statistics. An example of practical application of this theorem in Health Systems is to consider the existence of false positives and false negatives in diagnoses. At the Academy, the theme of BT is exposed almost exclusively in its analytical form. With this article, the authors intend to contribute to clarify the logic behind this theorem, and get students better understanding of its important fields of application, using three methods: the classic analytical (Bayesian inference), the frequentist (frequency inference) and the numerical simulation of Monte-Carlo. Thus, it intends to explain BT on a practical and friendly way that provides understanding to students avoiding memorizing the formulas. We provide a spreadsheet that is accessible to any professor. Moreover, we highlight the methodology could be extended to other topics. Author Keywords. Bayes, Monte-Carlo Simulation, False Positives, False Negatives, Engineering Education,Computation
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    Economic disposal quantity of leftovers kept in storage: a Monte Carlo simulation method
    (Edições Universitárias Lusófonas, 2019) Assis, Rui; Marques, Pedro Carmona; Santos, José Oliveira; Vidal, Raphaela; Faculdade de Engenharia
    This article describes how to reach an item’s threshold, or in other words, the limit time for it to be re 5 trieved from stock and sold for a different use, as well as the remaining foreseen period for this situation to occur. Once a minimum length, or weight, is reached, left quan tities are more difficult to sell, as demand often exceeds the remaining parts or leftovers. The number of unfulfilled 10 orders increases, as time goes by, until it becomes further cost effective to dispose the leftover and sell it for a lower price and alternative use. A Monte Carlo simulation model was built in order to consider the randomness of future transactions and quantifying consequences providing this 15 way a simple and effective decision-making. KEYWORDS: Decision-making ; Economical Optimization ; Monte-Carlo Simulation ; Stochastic Process