Using time and stochastic models for software reliability forecasting and analysis

Tämä työ luo katsauksen ajallisiin ja stokastisiin ohjelmien luotettavuus malleihin sekä tutkii muutamia malleja käytännössä. Työn teoriaosuus sisältää ohjelmien luotettavuuden kuvauksessa ja arvioinnissa käytetyt keskeiset määritelmät ja metriikan sekä varsinaiset mallien kuvaukset. Työssä esitellään kaksi ohjelmien luotettavuusryhmää. Ensimmäinen ryhmä ovat riskiin perustuvat mallit. Toinen ryhmä käsittää virheiden ”kylvöön” ja merkitsevyyteen perustuvat mallit. Työn empiirinen osa sisältää kokeiden kuvaukset ja tulokset. Kokeet suoritettiin käyttämällä kolmea ensimmäiseen ryhmään kuuluvaa mallia: Jelinski-Moranda mallia, ensimmäistä geometrista mallia sekä yksinkertaista eksponenttimallia. Kokeiden tarkoituksena oli tutkia, kuinka syötetyn datan distribuutio vaikuttaa mallien toimivuuteen sekä kuinka herkkiä mallit ovat syötetyn datan määrän muutoksille. Jelinski-Moranda malli osoittautui herkimmäksi distribuutiolle konvergaatio-ongelmien vuoksi, ensimmäinen geometrinen malli herkimmäksi datan määrän muutoksille.

Stochastic particle models: mean reversion and burgers dynamics. An application to commodity markets

The aim of this study is to propose a stochastic model for commodity markets linked with the Burgers equation from fluid dynamics. We construct a stochastic particles method for commodity markets, in which particles represent market participants. A discontinuity in the model is included through an interacting kernel equal to the Heaviside function and its link with the Burgers equation is given. The Burgers equation and the connection of this model with stochastic differential equations are also studied. Further, based on the law of large numbers, we prove the convergence, for large N, of a system of stochastic differential equations describing the evolution of the prices of N traders to a deterministic partial differential equation of Burgers type. Numerical experiments highlight the success of the new proposal in modeling some commodity markets, and this is confirmed by the ability of the model to reproduce price spikes when their effects occur in a sufficiently long period of time.

New directions in Stochastic Multicriteria Acceptability Analysis

Decisions taken in modern organizations are often multi-dimensional, involving multiple decision makers and several criteria measured on different scales. Multiple Criteria Decision Making (MCDM) methods are designed to analyze and to give recommendations in this kind of situations. Among the numerous MCDM methods, two large families of methods are the multi-attribute utility theory based methods and the outranking methods. Traditionally both method families require exact values for technical parameters and criteria measurements, as well as for preferences expressed as weights. Often it is hard, if not impossible, to obtain exact values. Stochastic Multicriteria Acceptability Analysis (SMAA) is a family of methods designed to help in this type of situations where exact values are not available. Different variants of SMAA allow handling all types of MCDM problems. They support defining the model through uncertain, imprecise, or completely missing values. The methods are based on simulation that is applied to obtain descriptive indices characterizing the problem. In this thesis we present new advances in the SMAA methodology. We present and analyze algorithms for the SMAA-2 method and its extension to handle ordinal preferences. We then present an application of SMAA-2 to an area where MCDM models have not been applied before: planning elevator groups for high-rise buildings. Following this, we introduce two new methods to the family: SMAA-TRI that extends ELECTRE TRI for sorting problems with uncertain parameter values, and SMAA-III that extends ELECTRE III in a similar way. An efficient software implementing these two methods has been developed in conjunction with this work, and is briefly presented in this thesis. The thesis is closed with a comprehensive survey of SMAA methodology including a definition of a unified framework.

Convergence withEU environmental legislation in Russia and Kyoto Protocol effects on certain power plants in Russia

Venäjällä uudistetaan sähkömarkkinoita. Uudistamisella pyritään vapauttamaan sähkömarkkinat ja lisäämään kilpailua energiasektorilla. Sähkömarkkinoiden vapauttamisen tarkoitus on energiasektorin hyötysuhteen nostaminen ja investointien houkutteleminen sektorille. Venäjä on ratifioinut Kioton protokollan, mikä energiasektorin kannalta on tärkeää, koska protokollan yhteistoteutusmekanismin kautta saadaan houkuteltua investointeja sektorille. Venäjän sähkömarkkinoiden vapauttamisen pitkäaikainen tähtäin on Venäjän ja Euroopan sähkömarkkinoiden integroituminen, joka tarkoittaa myös ympäristölainsäädännönyhtenäistämistä. Tämä tutkimus on osa Fortum Oyj:n tarjoamaa teknistä katselmusta Venäjällä toimivalle sähköyhtiölle, TGC-9:lle. Tässä työssä keskitytään TGC-9:n omistamien energiatuotantolaitoksien happamoitumista aiheuttaviin ilmapäästöihin ja pölypäästöihin. Tutkimuksessa pyritään myös löytämään Kioton protokollan yhteistoteutusmekanismi hyödyntämiskohteita. NOx -päästöt tulevat olemaan suurin haaste TGC-9:lle, jos ympäristöstandardit yhdenmukaistetaan. Yhteistoteutusmekanismin hyödyntämiskohteita löydettiin neljä: koksaamokaasun hyödyntäminen, maakaasun korvaaminen kuoren poltolla ja kaksi tapausta liittyen laitoksien hyötysuhteen nostamiseen.

On convergence of transforms based on parabolic scaling

This thesis studies properties of transforms based on parabolic scaling, like Curvelet-, Contourlet-, Shearlet- and Hart-Smith-transform. Essentially, two di erent questions are considered: How these transforms can characterize H older regularity and how non-linear approximation of a piecewise smooth function converges. In study of Hölder regularities, several theorems that relate regularity of a function f : R2 → R to decay properties of its transform are presented. Of particular interest is the case where a function has lower regularity along some line segment than elsewhere. Theorems that give estimates for direction and location of this line, and regularity of the function are presented. Numerical demonstrations suggest also that similar theorems would hold for more general shape of segment of low regularity. Theorems related to uniform and pointwise Hölder regularity are presented as well. Although none of the theorems presented give full characterization of regularity, the su cient and necessary conditions are very similar. Another theme of the thesis is the study of convergence of non-linear M ─term approximation of functions that have discontinuous on some curves and otherwise are smooth. With particular smoothness assumptions, it is well known that squared L2 approximation error is O(M-2(logM)3) for curvelet, shearlet or contourlet bases. Here it is shown that assuming higher smoothness properties, the log-factor can be removed, even if the function still is discontinuous.

Understanding change processes in business networks : a study of convergence in Finnish telecommunications 1985-2005

Aktörer inom telekommunikationsbranschen i Finland har genomgått en intensiv förändring under de senaste 25 åren, från 1980-talets självständiga företag till företag beroende av varandra, och även av aktörer inom närliggande branscher. I dag skapas telekommunikationsmarknaden inte endast av operatörerna, utan också av mediebolag (t.ex. MTV Media) och IT-företag (t.ex. TietoEnator). Gränserna mellan olika industrier håller därmed på att suddas ut - ett fenomen som allmänt benämns som teknologisk konvergens. Konvergens innebär att någonting integreras; det kan handla om t.ex. teknologier (telefoni och Internet), företag (AOL och Time Warner), industrier (telekom, media och IT-branscherna), tjänster (mobilt TV), produkter (PDA) osv. Detta innebär att ytterst få telekomaktörer ensamma kan vidareutveckla marknaden och tekniska lösningar. Samarbete mellan aktörer krävs; mobiltelefontillverkare, innehållsproducenter, operatörer osv. bör intesifiera sitt samarbete för att kunna erbjuda attraktiva tjänster och produkter till kunder och slutanvändare. Avhandlingen fokuserar speciellt på affärsnätverk och samarbetsmönster mellan nätverksaktörer som medel för att få tillgång till resurser som krävs i en konvergenskarakteriserad affärsomgivning. Avhandlingen lyfter fram vad den teknologiska konvergensen har inneburit för telekomaktörer, dvs. att företag tvingats förändra sina strategier och verksamhetsmodeller. För många företag i branschen har anpassningen till konvergenstänkande varit utmanande, och i vissa fall kan man till och med tala om att företagen upplevt en identitetskris. Den utförda forskningen visar att konvergens uppfattas på marknaden som en pågående förändringsprocess, där varje telekomaktör är tvungen att utvärdera sin roll och position i relation till andra aktörer inom branschen. Konvergensprocesser forsätter i framtiden med ökad intensitet. Aktörerna skapar medvetet sin omgivning genom att agera i olika roller, som kan sträcka sig över industrigränser. Avhandlingen påvisar även att externa händelser och industrikontexten påverkar dynamiken i ett affärsnätverk.

Convergence and divergence in global economy and social development : global perspectives on the contents of economic and soical development policy and its effects on rich and poor countries

The study of convergence and divergence in global economy and social development utilises comparative indicators to investigate the contents of economic and social development policy and their effects on the global samples that represent the rich industrial, semi-industrial and the poor developing nations. The study searchesfor answers to questions such as "what are the objectives of economic growth policies in globalisation under the imperatives of convergence and divergence, and how do these affect human well-being in consideration to the objectives of social policy in various nations?" The empirical verification of data utilises the concepts of the `logic of industrialism´ for comparative analysis that focuses mainly on identifying the levels of well-being in world nations after the Second World War. The perspectives of convergence and divergence in global economy and social development critically examine the stages of early development processes in global economy, distinguish the differences between economy and social development, illustrate the contents of economic and social development policies, their effects on rich and poor countries, and the nature of convergence and divergence in propelling economic growth and unequal social development in world nations. The measurement of convergence and divergence in global economy and social development utilised both economic and social data that were combined into an index that measures the precise levels of the effects of economic and social development policies on human well-being in the rich and poor nations. The task of finding policy solutions to resolve the controversies are reviewed through empirical investigations and the analyses of trends indicated within economic and social indicators and data. These revealed how the adoption of social policy measures in translating the gains from economic growth, towards promoting education, public health, and equity, generate social progress and longer life expectancy, higher economic growth, and sustain more stable macro economy for the nations. Social policy is concerned with the translation of benefits from objectives of global economic growth policies, to objectives of social development policy in nation states. Social policy, therefore, represents an open door whereby benefits of economic growth policies are linked with the broader objectives of social development policy, thereby enhancing the possibility of extending benefits from economic growth to all human being in every nation.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Convergence in Industry Evolution

The objective of the thesis is to enhance understanding of the evolution of convergence. Previous research has shown that the technological interfaces between distinct industries are one of the major sources of new radical cross-industry innovations. Despite the fact that convergence in industry evolution has attracted a substantial managerial interest, the conceptual confusion within the field of convergence exists. Firstly, this study clarifies the convergence phenomenon and its impact to industry evolution. Secondly, the study creates novel patent analysis methods to analyze technological convergence and provide tools for anticipating the early stages of convergence. Overall the study combines the industry evolution perspective and the convergence view of industrial evolution. The theoretical background for the study consists of the industry life cycle theories, technology evolution, and technological trajectories. The study links several important concepts in analyzing industry evolution, technological discontinuities, path-dependency, technological interfaces as a source of industry transformation, and the evolutionary stagesof convergence. Based on reviewing the literature a generic understanding of industry transformation and industrial dynamics was generated. In the convergence studies, the theoretical basis is in the discussion of different convergence types and their impacts on industry evolution, and in anticipating and monitoring the stages of convergence. The study is divided in two parts. The first part gives a general overview, and the second part comprises eight research publications. Our case study is based historically on two very distinct industries of the paper and electronics companies as a test environment to evaluate the importance of emerging business sectors and technological convergence as a source of industry transformation. Both qualitative and quantitative research methodology are utilized. The results of this study reveal that technological convergence and complementary innovations from different fields have significant effect to the emerging new business sector formation. The patent-based indicators in the analysis of technological convergence can be utilized on analyzing technology competition, capability and competence development, knowledge accumulation, knowledge spill-overs, and technology-based industry transformation. The patent-based indicators can provide insights to the future competitive environment. Results and conclusions from empirical part seem not be in conflict with real observations in the industry.

Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

The time series convergence of dispersion in financial analysts' forecasts

Finansanalytiker har en stor betydelse för finansmarknaderna, speciellt igenom att förmedla information genom resultatprognoser. Typiskt är att analytiker i viss grad är oeniga i sina resultatprognoser, och det är just denna oenighet analytiker emellan som denna avhandling studerar. Då ett företag rapporterar förluster tenderar oenigheten gällande ett företags framtid att öka. På ett intuitivt plan är det lätt att tolka detta som ökad osäkerhet. Det är även detta man finner då man studerar analytikerrapporter - analytiker ser ut att bli mer osäkra då företag börjar gå med förlust, och det är precis då som även oenigheten mellan analytikerna ökar. De matematisk-teoretiska modeller som beskriver analytikers beslutsprocesser har däremot en motsatt konsekvens - en ökad oenighet analytiker emellan kan endast uppkomma ifall analytikerna blir säkrare på ett individuellt plan, där den drivande kraften är asymmetrisk information. Denna avhandling löser motsägelsen mellan ökad säkerhet/osäkerhet som drivkraft bakom spridningen i analytikerprognoser. Genom att beakta mängden publik information som blir tillgänglig via resultatrapporter är det inte möjligt för modellerna för analytikers beslutsprocesser att ge upphov till de nivåer av prognosspridning som kan observeras i data. Slutsatsen blir därmed att de underliggande teoretiska modellerna för prognosspridning är delvis bristande och att spridning i prognoser istället mer troligt följer av en ökad osäkerhet bland analytikerna, i enlighet med vad analytiker de facto nämner i sina rapporter. Resultaten är viktiga eftersom en förståelse av osäkerhet runt t.ex. resultatrapportering bidrar till en allmän förståelse för resultatrapporteringsmiljön som i sin tur är av ytterst stor betydelse för prisbildning på finansmarknader. Vidare används typiskt ökad prognosspridning som en indikation på ökad informationsasymmetri i redovisningsforskning, ett fenomen som denna avhandling därmed ifrågasätter.