Teaching Monte Carlo Simulation with Python

Microsoft Excel and Google Sheets are powerful and widely used. But there’s a right way and a wrong way to use them, data scientists say. Microsoft Excel and Google Sheets are powerful and widely used. But there’s a right way and a wrong way to use them, data scientists say.

Statistical mechanical calculations with the extended reference interaction site model (XRISM) and Monte Carlo simulations are performed on fluid CF4. The parameters of a five‐center (12‐6) Lennard Jones potential with added point charges are deduced. The results are compared with total atom pair correlation functions recently determined by neutron diffraction experiments at three supercritical densities. Apart from the good agreement between experimental and theoretical results, we found that depending on the method used, different potentials are able to describe the investigated system. The atom pair correlation functions and angular distribution functions indicate a completely disordered arrangement of the molecules in fluid CF4 with no significant short‐range orientational order except for very close distances. The comparison of the results with former Reverse Monte Carlo (RMC) simulations allows the conclusion that the RMC method is an effective tool, equivalent to the well established Monte Carlo and XRISM methods, in determining the microscopic structure of fluid CF4, but without the exact knowledge of intermolecular potentials.

Some Thoughts About Data Type, Distribution, and Statistical Significance

Input modelling is a complex task within the Monte Carlo simulation, especially when the systems and processes under investigation reveal the non-linear behavior of several interdependent variables. Commonly used approaches for Monte Carlo simulation input modelling include selecting probability distributions and fitting them to existing data; resampling random variates from historical data; and using real-world data as an input model, which in the age of big data becomes more feasible. Each of the approaches comes with its own set of drawbacks. This note aims to describe a new method of input modelling for GDP Monte Carlo simulation based on interpolation of the GDP historical records. Also, this method has been implemented as a publicly available online tool using the Microsoft Azure Machine Learning Studio. A similar approach can be applied to other macroeconomic indicators, e.g., consumer price index (inflation) or current employment statistics. This note is intended for economists, data scientists, and operations research analysts interested in the GDP Monte Carlo simulation. It can also be used by academics, researchers, and practitioners in a broad subject area for generating input data for Monte Carlo simulation. Specifically, it can be of interest for Ph.D. candidates (VAC specialty 5.2.6) performing development of theory and methods of decision-making in economic and social systems, and application of artificial intelligence and big data methods in management.

In the construction of an office building in Indonesia, a delay occurred in the first-floor concrete work. The project was planned to be completed in 161 days but it took 168 days to complete. This study analyzed the issues that arose by doing a scheduling analysis and by comparing the Monte Carlo Simulation and the Statistic method. A comparison between these methods was carried out to determine the project duration scheduling probability level from each method. The data included in the study were the project’s Time Schedule and the interview data from respondents who were directly related to the office building construction project. Monte Carlo Simulation analysis showed that to get a 100% probability level, the duration can only be accelerated by two days, from 168 days to 166 days, while the Statistical method analysis showed that to get a 99.99% probability level the duration can only be accelerated by 4,2 days, from 168 days to 163.8 days. From the results above, it can be concluded that the Monte Carlo Simulation has a more accurate calculation than the Statistic Method because the result from the Monte Carlo Simulation was 166 days with a 100% probability level. It is close to the project realization duration which was 168 days. Keywords: construction delay analysis, monte carlo simulation, statistic method comparison

Business intelligence (BI) has traditionally functioned as a descriptive and diagnostic decision-support system, relying largely on structured and historical data to evaluate past organizational performance. However, increasing data volume, velocity, and variety, coupled with the demand for faster and forward-looking decisions, have revealed significant limitations in conventional BI architectures. This paper examines the evolution of business intelligence from hindsight-driven reporting systems to predictive and augmented intelligence platforms through the integration of data science techniques. Drawing exclusively on secondary literature published from 2015 onward, the study explores how machine learning, automation, real-time analytics, and augmented analytics are reshaping BI capabilities and organizational decision-making processes. A conceptual and comparative analysis is conducted between traditional BI systems and modern augmented analytics platforms, focusing on analytical depth, data processing architecture, user accessibility, and strategic value creation. The findings indicate that the convergence of BI and data science enables predictive foresight, reduces dependence on technical expertise, and supports real-time strategic decision-making. By narrowing the gap between data availability and actionable intelligence, modern BI systems evolve from retrospective reporting tools into proactive, enterprise-wide strategic assets. This study contributes to existing literature by clarifying the strategic role of data science in redefining business intelligence and enhancing organizational agility.

Training programs for statisticians and data scientists in healthcare should give greater importance to fostering inductive reasoning toward developing a mindset for optimizing Big Data. This can complement the current predominant focus on the hypothetico-deductive reasoning model, and is theoretically supported by the constructivist philosophy and Gestalt theory. Big-Data analytics is primarily exploratory in nature, aimed at discovery and innovation, and this requires fluid or inductive reasoning, which can be facilitated by epidemiological concepts (taxonomic and causal) as intuitive theories. Pedagogical strategies such as problem-based learning (PBL) and cooperative learning can be effective in this regard. Empirical research is required to ascertain instructors’ and practitioners’ perceptions about the role of inductive reasoning in Big-Data analytics, what constitutes effective pedagogy, and how core epidemiological concepts interact with the evidence from Big Data to produce outcomes. Together these can support the development of guidelines for an effective integrated curriculum for the training of statisticians and data scientists First published February 2020 at Statistics Education Research Journal Archives

Previous articleNext article FreeContributorsPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreMiloš Broćić is a Ph.D. candidate from the Department of Sociology at the University of Toronto. His work lies at the intersection of political sociology, culture, and sociological theory. His primary research interests include exploring the social bases of moral conflict and assessing intellectual movements in sociology.Jonah Stuart Brundage is assistant professor of sociology at the University of Michigan. His research interests include historical and comparative sociology, political sociology, economic sociology, and social theory. He is currently writing a book on the role of diplomats and treaty-making in the rise of the British Empire.Kristopher Velasco is assistant professor of sociology at Princeton University. He uses the case of LGBT+ rights to illuminate changes within international organizations, transnational processes, and world culture. Kristopher received his B.A. from the University of Kansas and M.A. and Ph.D. from the University of Texas at Austin.Carly R. Knight is assistant professor of sociology at New York University. She is an economic sociologist with interest in organizations, law, culture, and markets. Her research examines the history of American corporate capitalism and the cultural processes through which economic actors are constructed.Adam Goldstein is assistant professor jointly appointed in the Department of Sociology and the School of Public and International Affairs at Princeton University. His primary research focuses on the social consequences of financial capitalism in the United States. He holds a Ph.D. in sociology from the University of California, Berkeley.Geoffrey T. Wodtke is associate professor of sociology at the University of Chicago and associate director of the Stone Center for Research on Wealth Inequality and Mobility at the Harris School of Public Policy.Ugur Yildirim is a data scientist at Slingshot Finance. He earned his Ph.D. in Sociology from the University of California, Berkeley, in 2020.David J. Harding is professor of sociology at University of California, Berkeley, and the faculty director of the Berkeley D-Lab.Emma Zang, Ph.D., is assistant professor of sociology, biostatistics, and global affairs at Yale University. Her research interests lie at the intersection of health and aging, family demography, and inequality. She is also interested in developing and evaluating statistical methods to model trajectories and life transitions. She has published articles in the top sociology, demography, family, methodology, and health journals.Poh Lin Tan is an assistant professor at the Lee Kuan Yew School of Public Policy, National University of Singapore. Her research focuses on women’s fertility, health and work-life balance in the contexts of extremely low fertility rates in Singapore and Asia, with publications across sociology, demography, economics and social policy journals. She serves as the secretary of the Population Association of Singapore.Philip J. Cook is professor emeritus of public policy and economics at Duke University. His research interests include education, criminal behavior, income inequality, and other topics. Previous articleNext article DetailsFiguresReferencesCited by American Journal of Sociology Volume 128, Number 5March 2023 Article DOIhttps://doi.org/10.1086/725539 © 2023 The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.

Previous articleNext article No AccessNew Biological BooksComputer Presentation of Data in Science: A Do-It Yourself Guide, Based on the Apple Macintosh, for Authors and Illustrators in the Sciences. Doig Simmonds , Linda Reynolds John T. SorensenJohn T. Sorensen Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 66, Number 1Mar., 1991 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/417132 Copyright 1991 The University of Chicago PressPDF download Crossref reports no articles citing this article.

Continental Physiography, Climate, and the Global Distribution of Human Population

Problem statement: The main problem in solving of the Boltzmann equation by statistical methods is its long computational time (CPU time). The problem of decreasing of CPU time usage in statistical solution methods of Boltzmann equation for rarefied vortex flows was studied. Approach: In this study the Boltzmann equation in a rarefied Taylor-Quette flow regime was solved using the new Monte Carlo method which is officially named time Relaxated Mont Carlo method that applied the equilibrium conditions in each time step. Results: The results obtained from time Relaxated Mont Carlo method for the problem at hand were compared with those from the usual Direct Simulation Monte Carlo method. This comparison showed good agreement between the two sets of results. Conclusion: Whereas, the number of collisions and CPU usage time in the suggested method, as compared to the direct simulation Monte Carlo method, showed a significant decrease.

Two Pairs of Families of Estimators for Transport Problems

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.