京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第22514号 / 農博第2418号 / 新制||農||1078(附属図書館) / 学位論文||R2||N5294(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 村上 章, 教授 藤原 正幸, 教授 磯 祐介 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

This thesis is focused on possibilities of a project development prediction and a decision support for managers of those projects, which is an up to date topic in the present time turbulent environment. Project is understood as a stochastic process with discrete states and discrete time in this thesis. This approach could be represented by discrete moments of finding out project state. Project is compared to a finite automaton and Markovs chains are subsequently used. State model of the project based on Earned Value Management method is created in the proposal part of this thesis and there are state transitions probabilities. There are adjustments of the model designed consequently so the model is capable to fit some concrete situation closely. Designed proposals are tested in different situations to prove their value in the experimental part of this work.

Dans cette thèse nous essayons de comprendre pourquoi le Principe de Maximisation de Production d'Entropie (MEP) donne de très bons résultats dans de nombreux domaines de la physique hors équilibre et notamment en climatologie. Pour ce faire nous étudions ce principe sur des systèmes jouets de la physique statistique qui reproduisent les comportements des modèles climatiques. Nous avons notamment travaillé sur l'Asymmetric Simple Exclusion Process (ASEP) et le Zero Range Process (ZRP). Ceci nous a permis tout d'abord de relier MEP à un autre principe qui est le principe de maximisation d'entropie de Kolmogorov-Sinai (MKS). De plus, l'application de MEP à ces systèmes jouets donne des résultats physiquement cohérents. Nous avons ensuite voulu étendre le lien entre MEP et MKS dans des systèmes plus compliqués avant de montrer que, pour les chaines de Markov, maximiser l'entropie de KS revenait à minimiser le temps que le système prend pour atteindre son état stationnaire (mixing time). En fin nous avons appliqué MEP à la convection atmosphérique. / In this thesis we try to understand why the maximum entropy production principlegives really good results in a wide range of Physics fields and notably in climatology. Thus we study this principle on classical toy models which mimic the behaviour of climat models. In particular we worked on the Asymmetric Simple Exclusion Process(ASEP) and on the Zero Range Process (ZRP). This enabled us first to connect MEP to an other principle which is the maximum Kolmogorov-Sinaï entropy principle (MKS). Moreover the application of MEP on these systems gives results that are physically coherent. We then wanted to extend this link between MEP and MKS in more complicated systems, before showing that, for Markov Chains, maximise the KS entropy is the same as minimise the time the system takes to reach its stationnary state (mixing time). Thus, we applied MEP to the atmospheric convection.

This thesis investigates dierent types of Monte Carlo estimators for use in computationof chemical system, mainly to be used in calculating surface growthand evolution of SiC. Monte Carlo methods are a class of algorithms using randomsampling to numerical solve problems and are used in many cases. Threedierent types of Monte Carlo methods are studied, a simple Monte Carlo estimatorand two types of Markov chain Monte Carlo Metropolis algorithm MonteCarlo and kinetic Monte Carlo. The mathematical background is given for allmethods and they are tested both on smaller system, with known results tocheck their mathematical and chemical soundness and on larger surface systemas an example on how they could be used

One of the most significant changes in the evolution of modern business management is that organizations no longer compete as individual entities in the market, but as interlocking supply chains. Markets are no longer simply trading desks but dynamic ecosystems where people, organizations and the environment interact. Products and associated materials and resources are links that bridge supply chains from upstream (sourcing and manufacturing) to downstream (delivering and consuming). The lifecycle of a product plays a critical role in supply chains. Supply chains may be composed by, designed around, and modified for products. Product-related issues greatly impact supply chains. Existing studies have advanced product management and product lifecycle management literature through dimensions of product innovation, product growth, product line extensions, product efficiencies, and product acquisition. Product deletion, rationalization, or reduction research is limited but is a critical issue for many reasons. Sustainability is an important reason for this managerial decision. This study, grounded from multiple literature streams in both marketing and supply chain fields, identified relations and propositions to form a firm-level analysis on the role of supply chains in organizational product deletion decisions. Interviews, observational and archival data from international companies (i.e.: Australia, China, India, and Iran) contributed to the empirical support as case studies through a grounded theory approach. Bayesian analysis, an underused empirical analysis tool, was utilized to provide insights into this underdeveloped research stream; and its relationship to qualitative research enhances broader methodological understanding. Gibbs sampler and reversible jump Markov chain Monte Carlo (MCMC) simulation were used for Bayesian analysis based on collected data. The integrative findings are exploratory but provide insights for a number of research propositions.

An exact solution for the valuation of the options of the European style can be obtained using the Black-Scholes model. However, some of the limitations of the Black-Scholes model are said to be inconsistent such as the constant volatility of the stock price which is not the case in real life. In this thesis, the Black-Scholes model is extended to a model where the volatility is fully stochastic and changing over time, modelled by Markov chain with three states - high, medium and low. Under this model, we price options of both types, European and American, using Monte Carlo simulation.

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden. An attractive alternative is the Integrated Nested Laplace Approximations (INLA) approach, which is faster when applied to the broad class of Latent Gaussian Models (LGMs). The method computes fast and empirically accurate deterministic posterior marginal approximations of the model's unknown parameters. In the first part of this thesis, we discuss how to extend the software's applicability to a joint posterior inference by constructing a new class of joint posterior approximations, which also add marginal corrections for location and skewness. As these approximations result from a combination of a Gaussian Copula and internally pre-computed accurate Gaussian Approximations, we name this class Skew Gaussian Copula (SGC). By computing moments and correlation structure of a mixture representation of these distributions, we achieve new fast and accurate deterministic approximations for linear combinations in a subset of the model's latent field. The same mixture approximates a full joint posterior density through a Monte Carlo sampling on the hyperparameter set. We set highly skewed examples based on Poisson and Binomial hierarchical models and verify these new approximations using INLA and MCMC. The new skewness correction from the Skew Gaussian Copula is more consistent with the outcomes provided by the default INLA strategies. In the last part, we propose an extension of the parametric fit employed by the Simplified Laplace Approximation strategy in INLA when approximating posterior marginals. By default, the strategy matches log derivatives from a third-order Taylor expansion of each Laplace Approximation marginal with those derived from Skew Normal distributions. We consider a fourth-order term and adapt an Extended Skew Normal distribution to produce a more accurate approximation fit when skewness is large. We set similarly skewed data simulations with Poisson and Binomial likelihoods and show that the posterior marginal results from the new extended strategy are more accurate and coherent with the MCMC ones than its original version.

Seismic hazard analysis is concerned with estimating risk to human populations due to earthquakes and the other natural disasters that they cause. In many parts of the world, earthquake-generated tsunamis are especially dangerous. Assessing the risk for seismic disasters relies on historical data that indicate which fault zones are capable of supporting significant earthquakes. Due to the nature of geologic time scales, the era of seismological data collection with modern instruments has captured only a part of the Earth's seismic hot zones. However, non-instrumental records, such as anecdotal accounts in newspapers, personal journals, or oral tradition, provide limited information on earthquakes that occurred before the modern era. Here, we introduce a method for reconstructing the source earthquakes of historical tsunamis based on anecdotal accounts. We frame the reconstruction task as a Bayesian inference problem by making a probabilistic interpretation of the anecdotal records. Utilizing robust models for simulating earthquakes and tsunamis provided by the software package GeoClaw, we implement a Metropolis-Hastings sampler for the posterior distribution on source earthquake parameters. In this work, we present our analysis of the 1852 Banda Arc earthquake and tsunami as a case study for the method. Our method is implemented as a Python package, which we call tsunamibayes. It is available, open-source, on GitHub: https://github.com/jwp37/tsunamibayes.

This thesis investigates if the profitability of a wind farm can be increased by reducing itscontracted grid capacity. Two years of SCADA data is cleaned from non- and partialperformance which is used to estimate a wake reduced annual power time series. Stochasticmodels of production losses are applied to translate the wake reduced annual power timeseries. Ice losses are modelled with a 3-state Markov chain. The statistical properties arecalculated by identifying ice events in the SCADA. With the IEA task19 IceLoss algorithm areice events identified in the SCADA signal. An ice loss factor of 86 % is estimated for Juktanduring 2019. The results indicate that profitability can be increased by reducing the (contracted)grid capacity. Furthermore, the optimized grid capacity is shown to have low sensitivity to powerprice and ice losses. This finding is valuable since the power price market and weather areinherently difficult to predict. It follows that the prediction uncertainties of these inputs are lesssignificant when calculating the optimized grid capacity.

The objective of this thesis is to review the two popular mathematical models of the financialderivatives market. The models are the classical Black–Scholes–Merton and the Continuoustime Markov chain (CTMC) model. We study the CTMC model which is illustrated by themathematician Ragnar Norberg. The thesis demonstrates how the fundamental results ofFinancial Engineering work in both models.The construction of the main financial market components and the approach used for pricingthe contingent claims were considered in order to review the two models. In addition, the stepsused in solving the first–order partial differential equations in both models are explained.The main similarity between the models are that the financial market components are thesame. Their contingent claim is similar and the driving processes for both models utilizeMarkov property.One of the differences observed is that the driving process in the BSM model is the Brownianmotion and Markov chain in the CTMC model.We believe that the thesis can motivate other students and researchers to do a deeper andadvanced comparative study between the two models.