scholarly journals Design and Empirical Exploration for Second Trimester Period using Markov Process

2020 ◽  
Vol 8 (5) ◽  
pp. 3283-3285
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Factor ◽  
Markov Process ◽  
Stochastic Model ◽  
Empirical Analysis ◽  
Fetal Development ◽  
Behavioral Changes ◽  
Second Trimester ◽  
Different Levels

This research investigates the conditioned level in the mid-gestation period using stochastic model such as Markov process which requires the Monte Carlo simulation to get the intended results. The simulation in fetal stages addresses the influence of possible risk factor in different levels. The abnormal conditioned in mid-pregnancy that affects the behavioral randomness of the fetal development. The equation of the data implement through the Monte Carlo equation. Empirical Analysis has showed in the behavioral changes of fetal development during mid-gestation.

scholarly journals The Markov model as a pattern for earthquakes recurrence in South America

2001 ◽  
Vol 34 (4) ◽  
pp. 1611 ◽  
Author(s):  
T. M. TSAPANOS
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Process ◽  
South America ◽  
Markov Chains ◽  
Stochastic Model ◽  
Markov Model ◽  
Transition Matrix ◽  
Transition Probabilities ◽  
Large Earthquakes

The well known stochastic model of the Markov chains is applied in south America, in order to search for pattern of great earthquakes recurrence. The model defines a process in which successive state occupancies are governed by the transition probabilities pij, of the Markov process and are presented as a transition matrix say P, which has NxN dimensions. We considered as states in the present study the predefined seismic zones of south America. Thus the visits from zone to zone, which is from state to state, carry with them the number of the zone in which they occurred. If these visits are considered to be earthquake occurrences we can inspect their migration between the zones (states) and estimate their genesis in a statistical way, through the transition probabilities. Attention is given in zones where very large earthquakes with Ms>7.8 have occurred. A pattern is revealed which is suggested migration of these large shocks from south towards north. The use of Monte Carlo simulation verify the defined pattern.

scholarly journals Construction, Detection, and Interpretation of Crime Patterns over Space and Time

2020 ◽  
Vol 9 (6) ◽  
pp. 339
Author(s):  
Zengli Wang ◽  
Hong Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Empirical Studies ◽  
Prevention Strategies ◽  
Crime Patterns ◽  
Risk Levels ◽  
Research Areas ◽  
Research Objects ◽  
Crime Risk ◽  
Different Levels

Empirical studies have focused on investigating the interactive relationships between crime pairs. However, many other types of crime patterns have not been extensively investigated. In this paper, we introduce three basic crime patterns in four combinations. Based on graph theory, the subgraphs for each pattern were constructed and analyzed using criminology theories. A Monte Carlo simulation was conducted to examine the significance of these patterns. Crime patterns were statistically significant and generated different levels of crime risk. Compared to the classical patterns, combined patterns create much higher risk levels. Among these patterns, “co-occurrence, repeat, and shift” generated the highest level of crime risk, while “repeat” generated much lower levels of crime risk. “Co-occurrence and shift” and “repeat and shift” showed undulated risk levels, while others showed a continuous decrease. These results outline the importance of proposed crime patterns and call for differentiated crime prevention strategies. This method can be extended to other research areas that use point events as research objects.

Some Observations on the Random Response of an Elasto-Plastic System

1988 ◽  
Vol 55 (4) ◽  
pp. 911-917 ◽  
Author(s):  
L. G. Paparizos ◽  
W. D. Iwan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Brownian Motion ◽  
Markov Process ◽  
Practical Interest ◽  
Random Excitation ◽  
Process Models ◽  
Mean Square ◽  
Random Response ◽  
Monte Carlo Simulation Study

The nature of the response of strongly yielding systems subjected to random excitation, is examined. Special attention is given to the drift response, defined as the sum of yield increments associated with inelastic response. Based on the properties of discrete Markov process models of the yield increment process, it is suggested that for many cases of practical interest, the drift can be considered as a Brownian motion. The approximate Gaussian distribution and the linearly divergent mean square value of the process, as well as an expression for the probability distribution of the peak drift response, are obtained. The validation of these properties is accomplished by means of a Monte Carlo simulation study.

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

The specificities of risk identification and express estimation of investment projects in modern conditions

2019 ◽  
pp. 69-86
Author(s):  
Liana Chechenova ◽  
Natalya Volykhina ◽  
Yuriy Egorov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Factor ◽  
Investment Project ◽  
Simulation Method ◽  
Modern Concept ◽  
Monte Carlo Simulation Method ◽  
Investment Projects ◽  
Study Results ◽  
Project Risks

Objective: When assessing the risks of an investment project, it is necessary to take into account the uniqueness of each project, which requires the search for completely new solutions, the application and combination of various tools and assessment methods for the effective implementation of the project. The objective is to develop and test an algorithm for express risk assessment of an investment project using an integral risk factor. In order to achieve this the following issues are considered: the development of the risk theory and the main stages of its development, the modern concept of risk and risk classification in the context of investment projects, modern methods for assessing the risks of investment projects and their problems. Next, the concept of an integral risk factor is introduced and an express assessment of risks of a local investment project is carried out using an integral risk factor. Methods: The classical methods of identifying and assessing project risks (the method of expert estimates, the Monte-Carlo simulation method), the comparative method, analysis, and synthesis are used in the study. Results: The basic characteristics of the concept of “risk” inherent in the modern understanding were formulated. A new concept of the integral risk factor and the method of its calculation were proposed. Alternative scenarios for the implementation of the investment project using the integral risk factor were developed. An algorithm for complex risk express assessment of an investment project on a multi-brand dealer auto center construction was developed by means of an integral risk factor and Monte-Carlo simulation method. Practical importance: The developed algorithm can be used by managers to identify and evaluate or carry out an express assessment of complex risks of investment projects in the process of operating real projects, as well as to provide background for developing new and more advanced methods of project risks assessment.

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