Kalman Filter Algorithm for Rating and Prediction in Basketball

The Thesis presents a state-space model for a basketball league and a Kalman filter algorithm for the estimation of the state of the league. In the state-space model, each of the basketball teams is associated with a rating that represents its strength compared to the other teams. The ratings are assumed to evolve in time following a stochastic process with independent Gaussian increments. The estimation of the team ratings is based on the observed game scores that are assumed to depend linearly on the true strengths of the teams and independent Gaussian noise. The team ratings are estimated using a recursive Kalman filter algorithm that produces least squares optimal estimates for the team strengths and predictions for the scores of the future games. Additionally, if the Gaussianity assumption holds, the predictions given by the Kalman filter maximize the likelihood of the observed scores. The team ratings allow probabilistic inference about the ranking of the teams and their relative strengths as well as about the teams’ winning probabilities in future games. The predictions about the winners of the games are correct 65-70% of the time. The team ratings explain 16% of the random variation observed in the game scores. Furthermore, the winning probabilities given by the model are concurrent with the observed scores. The state-space model includes four independent parameters that involve the variances of noise terms and the home court advantage observed in the scores. The Thesis presents the estimation of these parameters using the maximum likelihood method as well as using other techniques. The Thesis also gives various example analyses related to the American professional basketball league, i.e., National Basketball Association (NBA), and regular seasons played in year 2005 through 2010. Additionally, the season 2009-2010 is discussed in full detail, including the playoffs.

Reconsidering Relativism and Intentionalism in Interpretation : Donald Davidson, Hermeneutics, and Pragmatism

This study focuses on two philosophical issues related to the interpretation of art. Firstly, it considers the role of authorial intentions in interpretation. Secondly, the study raises the issue of relativism in interpretation through a discussion of the relativistic tendencies apparent in the views of three major figures of contemporary philosophy: Joseph Margolis, Hans-Georg Gadamer, and Richard Rorty. The major goal of the thesis is to develop a theory of interpretation supporting the role of authorial intentions in interpretation on the basis of Donald Davidson s late philosophy of language and the holistic account of interpretation that underlies different parts of his philosophy. It is my belief that an intentionalist view of interpretation built on Davidsonian elements manages to form the most convincing defense of that interpretive position against the skepticism present in the views of Margolis, Gadamer, and Rorty. The theoretical issues addressed in the thesis are illuminated by discussions of case-examples, most importantly Richard Wagner s The Valkyrie, Thomas Adés America: A Prophecy, and some symphonies by Dimitri Shostakovich. In chapter one, I present a critical discussion of Margolis robust relativism. While finding Margolis criticism of the self-refutive argument plausible, I, nevertheless, argue that the relativistic logic Margolis offers should not be favored in interpretation. The first parts of chapter two outline Davidsonian intentionalism by presenting a reading of Davidson s later work in philosophy of language and mind, and by indicating its relationship to Davidson s views of literature. Then, I shall compare Davidson s ideas with some recent modest forms of intentionalism found in analytic aesthetics, and argue that Davidsonian intentionalism is in many respects more satisfactory compared to them. Chapter three engages Gadamer s hermeneutics by defending E.D. Hirsch s criticism of Gadamer. Uncovering the shortcomings in the replies of Gadamer s followers to Hirsch s criticism serves as a basis for the defense of intentionalism in interpretation carried out in the chapter. That defense is then extended with a discussion of some recent hermeneutic readings of Davidson s views. Chapter four deals with the standing of intentionalism through Rorty s pragmatist approach to literature. By indicating the position of pragmatist notions of aesthetic experience and imagination in Davidsonian intentionalism, it is shown that an intentionalist approach need not be as impoverished with regard to the value Rorty attributes to literature as he assumes. The concluding chapter outlines some ways in which one can be a pluralist with regard to art and interpretation without falling into relativism.

Stochastic Filtering

The stochastic filtering has been in general an estimation of indirectly observed states given observed data. This means that one is discussing conditional expected values as being one of the most accurate estimation, given the observations in the context of probability space. In my thesis, I have presented the theory of filtering using two different kind of observation process: the first one is a diffusion process which is discussed in the first chapter, while the third chapter introduces the latter which is a counting process. The majority of the fundamental results of the stochastic filtering is stated in form of interesting equations, such the unnormalized Zakai equation that leads to the Kushner-Stratonovich equation. The latter one which is known also by the normalized Zakai equation or equally by Fujisaki-Kallianpur-Kunita (FKK) equation, shows the divergence between the estimate using a diffusion process and a counting process. I have also introduced an example for the linear gaussian case, which is mainly the concept to build the so-called Kalman-Bucy filter. As the unnormalized and the normalized Zakai equations are in terms of the conditional distribution, a density of these distributions will be developed through these equations and stated by Kushner Theorem. However, Kushner Theorem has a form of a stochastic partial differential equation that needs to be verify in the sense of the existence and uniqueness of its solution, which is covered in the second chapter.

Essays on Russia's Economic Transition

This study comprises an introductory section and three essays analysing Russia's economic transition from the early 1990s up to the present. The papers present a combination of both theoretical and empirical analysis on some of the key issues Russia has faced during its somewhat troublesome transformation from state-controlled command economy to market-based economy. The first essay analyses fiscal competition for mobile capital between identical regions in a transition country. A standard tax competition framework is extended to account for two features of a transition economy: the presence of two sectors, old and new, which differ in productivity; and a non-benevolent regional decision-maker. It is shown that in very early phase of transition, when the old sector clearly dominates, consumers in a transition economy may be better off in a competitive equilibrium. Decision-makers, on the other hand, will prefer to coordinate their fiscal policies. The second essay uses annual data for 1992-2003 to examine income dispersion and convergence across 76 Russian regions. Wide disparities in income levels have indeed emerged during the transition period. Dispersion has increased most among the initially better-off regions, whereas for the initially poorer regions no clear trend of divergence or convergence could be established. Further, some - albeit not highly robust - evidence was found of both unconditional and conditional convergence, especially among the initially richer regions. Finally, it is observed that there is much less evidence of convergence after the economic crisis of 1998. The third essay analyses industrial firms' engagement in provision of infrastructure services, such as heating, electricity and road maintenance. Using a unique dataset of 404 large and medium-sized industrial enterprises in 40 regions of Russia, the essay examines public infrastructure provision by Russian industrial enterprises. It is found that to a large degree engagement in infrastructure provision, as proxied by district heating production, is a Soviet legacy. Secondly, firms providing district heating to users outside their plant area are more likely to have close and multidimensional relations with the local public sector.

Becoming to Know: Essays on Extended Epistemology of Knowledge Creation

This qualitative, explorative study, which comprises four essays, focuses on knowledge management (KM). It seeks to answer the question: How can the knowledge creation theory of KM benefit from social learning theories? While studying the five development phases of knowledge creation theory of KM through 1995-2008 and applying some social learning theories in essays, the concepts of knowing, learning and becoming have emerged. Drawing on these three concepts and on becoming ontology and extended epistemology as research philosophies the study suggests the ‘becoming epistemology’ concept and develops the ‘becoming to know’ framework. The framework proposes becoming as phronesis of dialectic interactions between learning and knowing. It shows how becoming to know evolves as an interplay between concrete experience and logical thinking in the present and in a living context. The proposed framework could be considered a contribution to the current development phase of the knowledge creation theory of KM because it illustrates how ontological and epistemological knowledge spirals come together, which is the essence of the knowledge creation theory of KM.

Systems between information and knowledge : In a memory management model of an extended enterprise

The research question of this thesis was how knowledge can be managed with information systems. Information systems can support but not replace knowledge management. Systems can mainly store epistemic organisational knowledge included in content, and process data and information. Certain value can be achieved by adding communication technology to systems. All communication, however, can not be managed. A new layer between communication and manageable information was named as knowformation. Knowledge management literature was surveyed, together with information species from philosophy, physics, communication theory, and information system science. Positivism, post-positivism, and critical theory were studied, but knowformation in extended organisational memory seemed to be socially constructed. A memory management model of an extended enterprise (M3.exe) and knowformation concept were findings from iterative case studies, covering data, information and knowledge management systems. The cases varied from groups towards extended organisation. Systems were investigated, and administrators, users (knowledge workers) and managers interviewed. The model building required alternative sets of data, information and knowledge, instead of using the traditional pyramid. Also the explicit-tacit dichotomy was reconsidered. As human knowledge is the final aim of all data and information in the systems, the distinction between management of information vs. management of people was harmonised. Information systems were classified as the core of organisational memory. The content of the systems is in practice between communication and presentation. Firstly, the epistemic criterion of knowledge is not required neither in the knowledge management literature, nor from the content of the systems. Secondly, systems deal mostly with containers, and the knowledge management literature with applied knowledge. Also the construction of reality based on the system content and communication supports the knowformation concept. Knowformation belongs to memory management model of an extended enterprise (M3.exe) that is divided into horizontal and vertical key dimensions. Vertically, processes deal with content that can be managed, whereas communication can be supported, mainly by infrastructure. Horizontally, the right hand side of the model contains systems, and the left hand side content, which should be independent from each other. A strategy based on the model was defined.

Chemical Characterization of Urban Background Aerosol Using Online And Filter Methods

In order to evaluate the influence of ambient aerosol particles on cloud formation, climate and human health, detailed information about the concentration and composition of ambient aerosol particles is needed. The dura-tion of aerosol formation, growth and removal processes in the atmosphere range from minutes to hours, which highlights the need for high-time-resolution data in order to understand the underlying processes. This thesis focuses on characterization of ambient levels, size distributions and sources of water-soluble organic carbon (WSOC) in ambient aerosols. The results show that in the location of this study typically 50-60 % of organic carbon in fine particles is water-soluble. The amount of WSOC was observed to increase as aerosols age, likely due to further oxidation of organic compounds. In the boreal region the main sources of WSOC were biomass burning during the winter and secondary aerosol formation during the summer. WSOC was mainly attributed to a fine particle mode between 0.1 - 1 μm, although different size distributions were measured for different sources. The WSOC concentrations and size distributions had a clear seasonal variation. Another main focus of this thesis was to test and further develop the high-time-resolution methods for chemical characterization of ambient aerosol particles. The concentrations of the main chemical components (ions, OC, EC) of ambient aerosol particles were measured online during a year-long intensive measurement campaign conducted on the SMEAR III station in Southern Finland. The results were compared to the results of traditional filter collections in order to study sampling artifacts and limitations related to each method. To achieve better a time resolution for the WSOC and ion measurements, a particle-into-liquid sampler (PILS) was coupled with a total organic carbon analyzer (TOC) and two ion chromatographs (IC). The PILS-TOC-IC provided important data about diurnal variations and short-time plumes, which cannot be resolved from the filter samples. In summary, the measurements made for this thesis provide new information on the concentrations, size distribu-tions and sources of WSOC in ambient aerosol particles in the boreal region. The analytical and collection me-thods needed for the online characterization of aerosol chemical composition were further developed in order to provide more reliable high-time-resolution measurements.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.