Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
E-governance in higher education contributes to enhancing community outreach; it can enhance transparency, efficiency, and accountability, as well as promotes social equity because e-governance's significance in higher education institutions goes beyond academia by linking the academicians with the wider community. This study aims to examine the contributions of e-governance in enhancing the community outreach in higher education institutions in Sudan. 776 respondents from three universities in Sudan contributed to the survey questionnaires. While eight informants from the same institutions answered interview questions. The interviews were analyzed using thematic analysis, while the questionnaires were analyzed using descriptive analysis. The findings revealed that e-governance supports community outreach in higher education institutions by facilitating communication between large segments of society and contributing to the development of digital citizenship. It is recommended that the universities should integrate e-governance to promote digital citizenship and online governance with the outside community. Besides higher education institutions should enhancing and encouraging the integration of information and communication technologies for community outreach.
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, we integrate GANs with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time-series model. In particular, we a develop new metric, (conditional) Sig-$W_1$, that captures the (conditional) joint law of time series models, and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators which alleviates the need for expensive training. Furthermore, we develop a novel generator, called the conditional AR-FNN, which is designed to capture the temporal dependence of time series and can be efficiently trained. We validate our method on both synthetic and empirical datasets and observe that our method consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.
A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.
<p>This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis.</p>
Sina Abbasi, Umar Muhammad Modibbo, Hamed Jafari Kolashlou
et al.
In the last several decades, Iran’s ecosystem has suffered due to the careless usage of natural resources. Cities have grown in an uneven and non-normative way, and poor project management has been a major issue, particularly in large cities. An even greater number of environmental factors and engineering regulations are not relevant to projects. Because of this, in order to ascertain a project’s environmental impact, an environmental impact assessment (EIA), is required. Using the rapid impact assessment matrix (RIAM) is one method of applying it to EIA. Reducing subjectivity brings objectivity and transparency. During the COVID-19 pandemic, a thorough EIA was carried out for the Tehran project utilizing the RIAM and other possibilities. This research is the first to combine the methodology that was discussed during the incident. Through the use of the RIAM technique, the environmental impact of COVID-19 was to be quantified in this inquiry. The research examined lockdown procedures and the COVID-19 pandemic to create an EIA indicator. In a real-world case study conducted in Tehran, Iran, the impact of the initiative was evaluated using the RIAM methodology during the COVID-19 epidemic. The results demonstrated that COVID-19 had both beneficial and harmful effects. Decision-makers were effectively informed about the COVID-19 pandemic’s environmental consequences on people and the environment, as well as how to minimize negative effects, according to the EIA technique that used RIAM. This is the first research to integrate the EIA during a crisis, such as the COVID-19 pandemic, with the RIAM approach.
This study aimed to construct a triangle for junior high school students through online learning based on the Ethnomathematics of Kalamata Fort Artifacts. This research is descriptive qualitative research with a case study type. The data collection methods used are concept understanding tests and interviews. The subjects of this study were three students who were grade VII junior high school students with high (S1), medium (S2), and low (S3) abilities. In this study, the data analysis conducted was descriptive. The data analyzed in this study are the results of concept understanding observations and interviews conducted by researchers on research subjects. The results showed that constructing the concept of triangles through online learning based on the Ethnomathematics of Kalamata Fort Artifacts had a good impact on the concept understanding ability of grade VII junior high school students.
Bengkulu Province is one of the provinces in Indonesia. Based on the results of the Population Census (SP) in September 2020, carried out by BPS, there were 2,010,670 inhabitants in Bengkulu Province. The area of Bengkulu Province is 19,813 km2, consisting of 10 regencies/cities. The large area and population encourage an effort to anticipate the transmission of COVID-19 that is soaring high in Bengkulu Province. One is by grouping regencies/cities in Bengkulu Province based on several variables that characterize objects using the Clustering method. This study aimed to group districts/cities in Bengkulu Province based on several variables that characterize objects related to the spread of COVID-19 in Bengkulu Province. The method used was the clustering method. The data used in this study was secondary data about the variable of the spread of COVID-19 in Bengkulu Province from January 1, 2021, to May 31, 2021. It is accessed through the official website of the Bengkulu Province government to convey information to the public regarding the increase of COVID-19 Cases in Bengkulu Province. The grouping using the Hierarchical Clustering method obtained the best model as complete linkage, with the number of clusters K = 2 and the K-Means method with K = 2. The results obtained are good because it has relatively tiny variability within the cluster, and the value of variability in both clusters is relatively large.
<p>In parts I and II of this paper series, rigorous tests for equality of stochastic processes were proposed. These tests provide objective criteria for deciding whether two processes differ, but they provide no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between multivariate stochastic processes. Like the tests, the diagnostics are framed in terms of vector autoregressive (VAR) models, which can be viewed as a dynamical system forced by random noise. The tests depend on two statistics, one that measures dissimilarity in dynamical operators and another that measures dissimilarity in noise covariances. Under suitable assumptions, these statistics are independent and can be tested separately for significance. If a term is significant, then the linear combination of variables that maximizes that term is obtained. The resulting indices contain all relevant information about differences between data sets. These techniques are applied to diagnose how the variability of annual-mean North Atlantic sea surface temperature differs between climate models and observations. For most models, differences in both noise processes and dynamics are important. Over 40 % of the differences in noise statistics can be explained by one or two discriminant components, though these components can be model dependent. Maximizing dissimilarity in dynamical operators identifies situations in which some climate models predict large-scale anomalies with the wrong sign.</p>
Chinwendu E. Madubueze, Isaac O. Onwubuya, Iorwuese Mzungwega
In this paper, a deterministic model for the transmission dynamics of measles infection with two doses of vaccination and isolation is studied. The disease-free equilibrium state and basic reproduction number,, of the model are computed. The sensitivity analysis of the model parameters is carried out using the Latin Hypercube Sampling (LHS) scheme in other to ascertain the crucial parameters that contribute to the spread of measles in the population. The result of the sensitivity analysis shown that transmission rates, vaccination rates and isolation of the infected persons in prodromal stage are significant parameters to be targeted for the eradication of measles infection. Based on the result of sensitivity analysis, the optimal control analysis is carried out using Pontryagin’s maximum principle to identify the optimal control strategies to be adopted by public health practitioners and policy health makers in curtailing the spread of measles infection. The result of the numerical simulations revealed that combined implementation of timely and correct administration of the two doses of vaccination, isolation of infected persons in prodromal stage and mass distribution of nutritional support will curtail the measles disease outbreak in the population. However, in a situation where there is limited facility to isolated the infected persons in prodromal stage, the combined implementation of mass distribution of nutritional support and administration of the two doses of vaccination will still eradicate measles infection in the population.
In the starlight atmospheric refraction navigation when the starlight transmits in the supersonic flow field, the aero-optical effect will reduce the accuracy of navigation. In this paper, the aircraft model is established by ICEM and Fluent is used to simulate the atmosphere density distribution at different altitudes and speeds. Then, the principle of geometric optics is used to track the starlight, the angular deviation of starlight transmission is deduced, and finally, the influence of different speeds and altitudes on starlight atmospheric refraction navigation is analyzed. The results show that the aero-optical effect produced by supersonic vehicles is related to the flight altitude and flight speed. Taking the flight altitude of 20 and 30 km as an example, when the flight speed is Mach 2, the angular deviation caused by the aero-optical effect is 1.045 and 0.699“ respectively, and when the flight speed is Mach 10, the angular deviation is 20.075 and 4.643”, respectively. Therefore, the aero-optical effect can be ignored at the altitude of 30 km and above. However, the influence of the aero-optical effect at 20 km needs to be judged according to the flight speed.
Iman Hazwam Abd Halim, Ammar Ibrahim Mahamad, Mohd Faris Mohd Fuzi
Technology has advanced to the point that it can assist people in their daily lives. Human beings may benefit from this development in a variety of ways. Progress in river water monitoring is also one of them. There are many advantages in improving the river water monitoring system. The objective of this project is to develop an automated system for monitoring river water levels and quality with push notification features. Internet of Things (IoT) was implemented in this research by using NodeMCU as a microcontroller to connect both ultrasonic sensors and pH sensors to the Internet. An ultrasonic sensor is used to read the water level, and a pH sensor is used to read the water pH values. The results show the successful output from all of 10 time attempts to obtain more accurate test results. The results will be averaged to be analysed and concluded from the test. All the tests include testing for the accuracy of the ultrasonic sensor, the accuracy of the pH sensor, and the performance of the internet connection using integrated Wi-Fi module in NodeMCU microcontroller. The system test also shows that it performs perfectly with the requirement needed to send the real-time status of the water level, water quality and an alert to the user using the Telegram Bot API. This research can help to increase the level of awareness of the river water monitoring system. This research was done by looking at people's problems in the vicinity of the river area by producing a system tool that helps to monitor the river water in real-time status.
In this paper, estimation of entropy for Weibull distribution based on record values is considered. Maximum likelihood estimation and Bayes estimation for Shannon entropy and Renyi entropy have been considered based on record values. Bayes estimators are obtained using Markov Chain Monte Carlo method. A simulation study is performed to find the performance of the estimators developed in this paper. The inferential procedures developed in this paper have also been illustrated using real data.
Jérémy Magnanensi, Jérémy Magnanensi, Myriam Maumy-Bertrand
et al.
Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection. Most of these techniques rely on tuning parameters that are often determined by cross-validation (CV) based methods, which raises essential stability issues. To overcome this, we have developed a new dynamic bootstrap-based method for significant predictor selection, suitable for both PLS regression and its incorporation into generalized linear models (GPLS). It relies on establishing bootstrap confidence intervals, which allows testing of the significance of predictors at preset type I risk α, and avoids CV. We have also developed adapted versions of sparse PLS (SPLS) and sparse GPLS regression (SGPLS), using a recently introduced non-parametric bootstrap-based technique to determine the numbers of components. We compare their variable selection reliability and stability concerning tuning parameters determination and their predictive ability, using simulated data for PLS and real microarray gene expression data for PLS-logistic classification. We observe that our new dynamic bootstrap-based method has the property of best separating random noise in y from the relevant information with respect to other methods, leading to better accuracy and predictive abilities, especially for non-negligible noise levels.
In this paper we study the solvability of the boundary value problem for the heat equation in a domain that degenerates into a point at the initial moment of time. In this case, the boundary changing with time moves according to an arbitrary law x = γ(t). Using the generalized heat potentials, the problem under study is reduced to a pseudo-Volterra integral equation such that the norm of the integral operator is equal to one and it is shown that the corresponding homogeneous integral equation has a nonzero solution.
Let {XN , N ≥ 1} be a sequence of strictly stationary associated random variables of interest, and {TN , N ≥ 1} be a sequence of random truncating variables assumed to be independent from {XN , N ≥ 1}. In this paper, we establish the strong uniform consistency with a rate of a kernel hazard rate function estimator, when the variable of interest is subject to random left truncation under association condition. Simulation results are also provided to evaluate the finite-sample performances of the proposed estimator.
In this paper, we accomplished the concept of continuous and discrete Hermite wavelet transforms. We also discussed some basic properties of Hermite wavelet transform. Inversion formula and Parsevals formula for continuous Hermite wavelet transform is established. Moreover the discrete version of wavelet transform is discussed.