Variance Reduction Techniques in Monte Carlo Methods

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values). Moreover there are some simulation models that represent rare events which have extremely small probabilities of occurrence), so even modern computer would take ’for ever’ (centuries) to execute a single run - were it not that special VRT can reduce theses excessively long runtimes to practical magnitudes.common random numbers;antithetic random numbers;importance sampling;control variates;conditioning;stratied sampling;splitting;quasi Monte Carlo

Probabilistic Modeling of Process Systems with Application to Risk Assessment and Fault Detection

Three new methods of joint probability estimation (modeling), a maximum-likelihood maximum-entropy method, a constrained maximum-entropy method, and a copula-based method called the rolling pin (RP) method, were developed. Compared to many existing probabilistic modeling methods such as Bayesian networks and copulas, the developed methods yield models that have better performance in terms of flexibility, interpretability and computational tractability. These methods can be used readily to model process systems and perform risk analysis and fault detection at steady state conditions, and can be coupled with appropriate mathematical tools to develop dynamic probabilistic models. Also, a method of performing probabilistic inference using RP-estimated joint probability distributions was introduced; this method is superior to Bayesian networks in several aspects. The RP method was also applied successfully to identify regression models that have high level of flexibility and are appealing in terms of computational costs.Ph.D., Chemical Engineering -- Drexel University, 201

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Studying protein-ligand interactions using a Monte Carlo procedure

[eng] Biomolecular simulations have been widely used in the study of protein-ligand interactions; comprehending the mechanisms involved in the prediction of binding affinities would have a significant repercussion in the pharmaceutical industry. Notwithstanding the intrinsic difficulty of sampling the phase space, hardware and methodological developments make computer simulations a promising candidate in the resolution of biophysically relevant problems. In this context, the objective of the thesis is the development of a protocol that permits studying protein-ligand interactions, in view to be applied in drug discovery pipelines. The author contributed to the rewriting PELE, our Monte Carlo sampling procedure, using good practices of software development. These involved testing, improving the readability, modularity, encapsulation, maintenance and version control, just to name a few. Importantly, the recoding resulted in a competitive cutting-edge software that is able to integrate new algorithms and platforms, such as new force fields or a graphical user interface, while being reliable and efficient. The rest of the thesis is built upon this development. At this point, we established a protocol of unbiased all-atom simulations using PELE, often combined with Markov (state) Models (MSM) to characterize the energy landscape exploration. In the thesis, we have shown that PELE is a suitable tool to map complex mechanisms in an accurate and efficient manner. For example, we successfully conducted studies of ligand migration in prolyl oligopeptidases and nuclear hormone receptors (NHRs). Using PELE, we could map the ligand migration and binding pathway in such complex systems in less than 48 hours. On the other hand, with this technique we often run batches of 100s of simulations to reduce the wall-clock time. MSM is a useful technique to join these independent simulations in a unique statistical model, as individual trajectories only need to characterize the energy landscape locally, and the global characterization can be extracted from the model. We successfully applied the combination of these two methodologies to quantify binding mechanisms and estimate the binding free energy in systems involving NHRs and tyorsinases. However, this technique represents a significant computational effort. To reduce the computational load, we developed a new methodology to overcome the sampling limitations caused by the ruggedness of the energy landscape. In particular, we used a procedure of iterative simulations with adaptive spawning points based on reinforcement learning ideas. This permits sampling binding mechanisms at a fraction of the cost, and represents a speedup of an order of magnitude in complex systems. Importantly, we show in a proof-of-concept that it can be used to estimate absolute binding free energies. Overall, we hope that the methodologies presented herein help streamline the drug design process.[spa] Las simulaciones biomoleculares se han usado ampliamente en el estudio de interacciones proteína-ligando. Comprender los mecanismos involucrados en la predicción de afinidades de unión tiene una gran repercusión en la industria farmacéutica. A pesar de las dificultades intrínsecas en el muestreo del espacio de fases, mejoras de hardware y metodológicas hacen de las simulaciones por ordenador un candidato prometedor en la resolución de problemas biofísicos con alta relevancia. En este contexto, el objetivo de la tesis es el desarrollo de un protocolo que introduce un estudio más eficiente de las interacciones proteína-ligando, con vistas a diseminar PELE, un procedimiento de muestreo de Monte Carlo, en el diseño de fármacos. Nuestro principal foco ha sido sobrepasar las limitaciones de muestreo causadas por la rugosidad del paisaje de energías, aplicando nuestro protocolo para hacer analsis detallados a nivel atomístico en receptores nucleares de hormonas, receptores acoplados a proteínas G, tirosinasas y prolil oligopeptidasas, en colaboración con una compañía farmacéutica y de varios laboratorios experimentales. Con todo ello, esperamos que las metodologías presentadas en esta tesis ayuden a mejorar el diseño de fármacos

Process query systems : advanced technologies for process detection and tracking

Vrijwel alles wat rondom ons heen gebeurt is van nature proces georienteerd. Het is dan niet verbazingwekkend dat het mentale omgevingsbeeld dat mensen van hun omgeving vormen hierop is gebaseerd. Zodra we iets waarnemen, en vervolgens herkennen, betekent dit dat we de waarneming begrijpen, ze bij elkaar kunnen groeperen, en voorspellen welke andere waarnemingen spoedig zullen volgen. Neem bijvoorbeeld een kamer met een televisie. Zodra we de kamer binnenkomen horen we geluiden, misschien stemmen, mischien muziek. Als we om ons heen kijken zien wij spoedig, visueel, de televisie. Omdat we het "proces" van TV goed kennen, kunnen we mentaal de geluiden bij het beeld van de televisie voegen. Ook weten we dat de telvisie aan is, en daarom verwachten we dat er nog meer geluiden zullen volgen. Zodra we de afstandsbediening oppakken en de televisie uitzetten, verwachten we dat het beeld verdwijnt en de geluiden ophouden. Als dit niet gebeurt, merken we dit direct op: we waren niet succesvol in het veranderen van de staat van het "proces TV". Over het algemeen, als onze waarnemingen niet bij een bekend proces passen zijn wij verbaasd, geinteresseerd, of zelfs bang. Dit is een goed voorbeeld van hoe mensen hun omgeving beschouwen, gebaseerd op processen classificeren we al onze waarnemingen, en zijn we in staat te voorspellen welke waarnemingen komen gaan. Computers zijn traditioneel niet in staat om herkenning op diezelfde wijze te realiseren. Computerverwerking van signalen is vaak gebaseerd op eenvoudige "signatures", ofwel enkelvoudige eigenschappen waar direct naar gezocht wordt. Vaak zijn deze systemen heel specifiek en kunnen slechts zeer beperkte voorspellingen maken inzake de waargenomen omgeving. Dit proefschrift introduceert een algemene methode waarin omgevingsbeschrijvingen worden ingevoerd als processen: een nieuwe klasse van gegevensverwerkende systemen, genaamd Process Query Systems (PQS). Een PQS stelt de gebruiker in staat om snel en efficient een robuust omgevingsbewust systeem te bouwen, dat in staat is meerdere processen en meerdere instanties van processen te detecteren en volgen. Met behulp van PQS worden verschillende systemen gepresenteerd zo divers als de beveiliging van grote computer netwerken, tot het volgen van vissen in een vistank. Het enige verschil tussen al deze systemen is de procesmodellen die ingevoerd werden in de PQS. Deze technologie is een nieuw en veelbelovend vakgebied dat het potentieel heeft zeer succesvol te worden in alle vormen van digitale signaalverwerking.UBL - phd migration 201

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

You Shall Not Pass! Measuring, Predicting, and Detecting Malware Behavior

Researchers have been fighting malicious behavior on the Internet for several decades. The arms race is far from being close to an end, but this PhD work is intended to be another step towards the goal of making the Internet a safer place. My PhD has focused on measuring, predicting, and detecting malicious behavior on the Internet; we focused our efforts towards three different paths: establishing causality relations into malicious actions, predicting the actions taken by an attacker, and detecting malicious software. This work tried to understand the causes of malicious behavior in different scenarios (sandboxing, web browsing), by applying a novel statistical framework and statistical tests to determine what triggers malware. We also used deep learning algorithms to predict what actions an attacker would perform, with the goal of anticipating and countering the attacker’s moves. Moreover, we worked on malware detection for Android, by modeling sequences of API with Markov Chains and applying machine learning algorithms to classify benign and malicious apps. The methodology, design, and results of our research are relevant state of the art in the field; we will go through the different contributions that we worked on during my PhD to explain the design choices, the statistical methods and the takeaways characterizing them. We will show how these systems have an impact on current tools development and future research trends

Sample-based Search Methods for Bayes-Adaptive Planning

A fundamental issue for control is acting in the face of uncertainty about the environment. Amongst other things, this induces a trade-off between exploration and exploitation. A model-based Bayesian agent optimizes its return by maintaining a posterior distribution over possible environments, and considering all possible future paths. This optimization is equivalent to solving a Markov Decision Process (MDP) whose hyperstate comprises the agent's beliefs about the environment, as well as its current state in that environment. This corresponding process is called a Bayes-Adaptive MDP (BAMDP). Even for MDPs with only a few states, it is generally intractable to solve the corresponding BAMDP exactly. Various heuristics have been devised, but those that are computationally tractable often perform indifferently, whereas those that perform well are typically so expensive as to be applicable only in small domains with limited structure. Here, we develop new tractable methods for planning in BAMDPs based on recent advances in the solution to large MDPs and general partially observable MDPs. Our algorithms are sample-based, plan online in a way that is focused on the current belief, and, critically, avoid expensive belief updates during simulations. In discrete domains, we use Monte-Carlo tree search to search forward in an aggressive manner. The derived algorithm can scale to large MDPs and provably converges to the Bayes-optimal solution asymptotically. We then consider a more general class of simulation-based methods in which approximation methods can be employed to allow value function estimates to generalize between hyperstates during search. This allows us to tackle continuous domains. We validate our approach empirically in standard domains by comparison with existing approximations. Finally, we explore Bayes-adaptive planning in environments that are modelled by rich, non-parametric probabilistic models. We demonstrate that a fully Bayesian agent can be advantageous in the exploration of complex and even infinite, structured domains