Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

Probabilistic Inductive Querying Using ProbLog

We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks.

Univariate, bivariate, and multivariate methods in corpus-based lexicography : A study of synonymy

In this dissertation, I present an overall methodological framework for studying linguistic alternations, focusing specifically on lexical variation in denoting a single meaning, that is, synonymy. As the practical example, I employ the synonymous set of the four most common Finnish verbs denoting THINK, namely ajatella, miettiä, pohtia and harkita ‘think, reflect, ponder, consider’. As a continuation to previous work, I describe in considerable detail the extension of statistical methods from dichotomous linguistic settings (e.g., Gries 2003; Bresnan et al. 2007) to polytomous ones, that is, concerning more than two possible alternative outcomes. The applied statistical methods are arranged into a succession of stages with increasing complexity, proceeding from univariate via bivariate to multivariate techniques in the end. As the central multivariate method, I argue for the use of polytomous logistic regression and demonstrate its practical implementation to the studied phenomenon, thus extending the work by Bresnan et al. (2007), who applied simple (binary) logistic regression to a dichotomous structural alternation in English. The results of the various statistical analyses confirm that a wide range of contextual features across different categories are indeed associated with the use and selection of the selected think lexemes; however, a substantial part of these features are not exemplified in current Finnish lexicographical descriptions. The multivariate analysis results indicate that the semantic classifications of syntactic argument types are on the average the most distinctive feature category, followed by overall semantic characterizations of the verb chains, and then syntactic argument types alone, with morphological features pertaining to the verb chain and extra-linguistic features relegated to the last position. In terms of overall performance of the multivariate analysis and modeling, the prediction accuracy seems to reach a ceiling at a Recall rate of roughly two-thirds of the sentences in the research corpus. The analysis of these results suggests a limit to what can be explained and determined within the immediate sentential context and applying the conventional descriptive and analytical apparatus based on currently available linguistic theories and models. The results also support Bresnan’s (2007) and others’ (e.g., Bod et al. 2003) probabilistic view of the relationship between linguistic usage and the underlying linguistic system, in which only a minority of linguistic choices are categorical, given the known context – represented as a feature cluster – that can be analytically grasped and identified. Instead, most contexts exhibit degrees of variation as to their outcomes, resulting in proportionate choices over longer stretches of usage in texts or speech.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Poverty and the Environment : Investment Poverty and the Role of Assets in Generating Welfare for Farmer Households in the Province of Herrera, Panama

Köyhiä maanviljelijöitä on usein syytetty kehitysmaiden ympäristöongelmista. On väitetty, että eloonjäämistaistelu pakottaa heidät käyttämään maata ja muita luonnonvaroja lyhytnäköisesti. Harva asiaa koskeva tutkimus on kuitenkaan tukenut tätä väitettä; perheiden köyhyyden astetta ja heidän aiheuttamaansa ympäristövaikutusta ei ole kyetty kytkemään toisiinsa. Selkeyttääkseen köyhyys-ympäristö –keskustelua, Thomas Reardon ja Steven Vosti kehittivät investointiköyhyyden käsitteen. Se tunnistaa sen kenties suuren joukon maanviljelijäperheitä, jotka eivät ole köyhiä perinteisten köyhyysmittareiden mukaan, mutta joiden hyvinvointi ei ole riittävästi köyhyysrajojen yläpuolella salliakseen perheen investoida kestävämpään maankäyttöön. Reardon ja Vosti korostivat myös omaisuuden vaikutusta perheiden hyvinvointiin, ja uskoivat sen vaikuttavan tuotanto- ja investointipäätöksiin. Tässä tutkimuksessa pyritään vastaamaan kahteen kysymykseen: Miten investointiköyhyyttä voidaan ymmärtää ja mitata? Ja, mikä on viljelijäperheiden omaisuuden hyvinvointia lisäävä vaikutus? Tätä tutkimusta varten haastateltiin 402 maanviljelijäperhettä Väli-Amerikassa, Panaman tasavallan Herreran läänissä. Näiden perheiden hyvinvointia mitattiin heidän kulutuksensa mukaan, ja paikalliset köyhyysrajat laskettiin paikallisen ruoan hinnan mukaan. Herrerassa ihminen tarvitsee keskimäärin 494 dollaria vuodessa saadakseen riittävän ravinnon, tai 876 dollaria vuodessa voidakseen ravinnon lisäksi kattaa muitakin välttämättömiä menoja. Ruoka- eli äärimmäisen köyhyyden rajan alle jäi 15,4% tutkituista perheistä, ja 33,6% oli jokseenkin köyhiä, eli saavutti kyllä riittävän ravitsemuksen, muttei kyennyt kustantamaan muita perustarpeitaan. Molempien köyhyysrajojen yläpuolelle ylsi siis 51% tutkituista perheistä. Näiden köyhyysryhmien välillä on merkittäviä eroavaisuuksia ei vain perheiden varallisuuden, tulojen ja investointistrategioiden välillä, mutta myös perheiden rakenteessa, elinympäristössä ja mahdollisuuksissa saada palveluja. Investointiköyhyyden mittaaminen osoittautui haastavaksi. Herrerassa viljelijät eivät tee investointeja puhtaasti ympäristönsuojeluun, eikä maankäytön kestävyyttä muutenkaan pystytty yhdistämään perheiden hyvinvoinnin tasoon. Siksi investointiköyhyyttä etsittiin sellaisena hyvinvoinnin tasona, jonka alapuolella elävien perheiden parissa tuottavat maanparannusinvestoinnit eivät enää ole suorassa suhteessa hyvinvointiin. Tällaisia investointeja ovat mm. istutetut aidat, lannoitus ja paranneltujen laiduntyyppien viljely. Havaittiin, että jos perheen hyvinvointi putoaa alle 1000 dollarin/henkilö/vuosi, tällaiset tuottavat maanparannusinvestoinnit muuttuvat erittäin harvinaisiksi. Investointiköyhyyden raja on siis noin kaksi kertaa riittävän ravitsemuksen hinta, ja sen ylitti 42,3% tutkituista perheistä. Heille on tyypillistä, että molemmat puolisot käyvät työssä, ovat korkeasti koulutettuja ja yhteisössään aktiivisia, maatila tuottaa paremmin, tilalla kasvatetaan vaativampia kasveja, ja että he ovat kerryttäneet enemmän omaisuutta kuin investointi-köyhyyden rajan alla elävät perheet. Tässä tutkimuksessa kyseenalaistettiin yleinen oletus, että omaisuudesta olisi poikkeuksetta hyötyä viljelijäperheelle. Niinpä omaisuuden vaikutusta perheiden hyvinvointiin tutkittiin selvittämällä, mitä reittejä pitkin perheiden omistama maa, karja, koulutus ja työikäiset perheenjäsenet voisivat lisätä perheen hyvinvointia. Näiden hyvinvointi-mekanismien ajateltiin myös riippuvan monista väliin tulevista tekijöistä. Esimerkiksi koulutus voisi lisätä hyvinvointia, jos sen avulla saataisiin paremmin palkattuja töitä tai perustettaisiin yritys; mutta näihin mekanismeihin saattaa vaikuttaa vaikkapa etäisyys kaupungeista tai se, omistaako perhe ajoneuvon. Köyhimpien perheiden parissa nimenomaan koulutus olikin ainoa tutkittu omaisuuden muoto, joka edisti perheen hyvinvointia, kun taas maasta, karjasta tai työvoimasta ei ollut apua köyhyydestä nousemiseen. Varakkaampien perheiden parissa sen sijaan korkeampaa hyvinvointia tuottivat koulutuksen lisäksi myös maa ja työvoima, joskin monesta väliin tulevasta muuttujasta, kuten tuotantopanoksista riippuen. Ei siis ole automaatiota, jolla omaisuus parantaisi perheiden hyvinvointia. Vaikka rikkailla onkin yleensä enemmän karjaa kuin köyhemmillä, ei tässä aineistossa löydetty yhtään mekanismia, jota kautta karjan määrä tuottaisi korkeampaa hyvinvointia viljelijäperheille. Omaisuuden keräämisen ja hyödyntämisen strategiat myös muuttuvat hyvinvoinnin kasvaessa ja niihin vaikuttavat monet ulkoiset tekijät. Ympäristön ja köyhyyden suhde on siis edelleen epäselvä. Köyhyyden voittaminen vaatii pitkällä tähtäimellä sitä, että viljelijäperheet nousisivat investointiköyhyyden rajan yläpuolelle. Näin heillä olisi varaa alkaa kartuttaa omaisuutta ja investoida kestävämpään maankäyttöön. Tällä hetkellä kuitenkin isolle osalle herreralaisia perheitä tuo raja on kaukana tavoittamattomissa. Miten päästä yli tuhannen dollarin kulutukseen perheenjäsentä kohden, mikäli elintaso ei yllä edes riittävään ravitsemukseen? Ja sittenkin, vaikka hyvinvointi kohenisi, ei ympäristön kannalta parannuksia ole välttämättä odotettavissa, mikäli karjalaumat kasvavat ja eroosioalttiit laitumet leviävät.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Bayesian Inference for Retrospective Population Genetics Models Using Markov Chain Monte Carlo Methods

Genetics, the science of heredity and variation in living organisms, has a central role in medicine, in breeding crops and livestock, and in studying fundamental topics of biological sciences such as evolution and cell functioning. Currently the field of genetics is under a rapid development because of the recent advances in technologies by which molecular data can be obtained from living organisms. In order that most information from such data can be extracted, the analyses need to be carried out using statistical models that are tailored to take account of the particular genetic processes. In this thesis we formulate and analyze Bayesian models for genetic marker data of contemporary individuals. The major focus is on the modeling of the unobserved recent ancestry of the sampled individuals (say, for tens of generations or so), which is carried out by using explicit probabilistic reconstructions of the pedigree structures accompanied by the gene flows at the marker loci. For such a recent history, the recombination process is the major genetic force that shapes the genomes of the individuals, and it is included in the model by assuming that the recombination fractions between the adjacent markers are known. The posterior distribution of the unobserved history of the individuals is studied conditionally on the observed marker data by using a Markov chain Monte Carlo algorithm (MCMC). The example analyses consider estimation of the population structure, relatedness structure (both at the level of whole genomes as well as at each marker separately), and haplotype configurations. For situations where the pedigree structure is partially known, an algorithm to create an initial state for the MCMC algorithm is given. Furthermore, the thesis includes an extension of the model for the recent genetic history to situations where also a quantitative phenotype has been measured from the contemporary individuals. In that case the goal is to identify positions on the genome that affect the observed phenotypic values. This task is carried out within the Bayesian framework, where the number and the relative effects of the quantitative trait loci are treated as random variables whose posterior distribution is studied conditionally on the observed genetic and phenotypic data. In addition, the thesis contains an extension of a widely-used haplotyping method, the PHASE algorithm, to settings where genetic material from several individuals has been pooled together, and the allele frequencies of each pool are determined in a single genotyping.

On the Rademacher maximal function

Tools known as maximal functions are frequently used in harmonic analysis when studying local behaviour of functions. Typically they measure the suprema of local averages of non-negative functions. It is essential that the size (more precisely, the L^p-norm) of the maximal function is comparable to the size of the original function. When dealing with families of operators between Banach spaces we are often forced to replace the uniform bound with the larger R-bound. Hence such a replacement is also needed in the maximal function for functions taking values in spaces of operators. More specifically, the suprema of norms of local averages (i.e. their uniform bound in the operator norm) has to be replaced by their R-bound. This procedure gives us the Rademacher maximal function, which was introduced by Hytönen, McIntosh and Portal in order to prove a certain vector-valued Carleson's embedding theorem. They noticed that the sizes of an operator-valued function and its Rademacher maximal function are comparable for many common range spaces, but not for all. Certain requirements on the type and cotype of the spaces involved are necessary for this comparability, henceforth referred to as the “RMF-property”. It was shown, that other objects and parameters appearing in the definition, such as the domain of functions and the exponent p of the norm, make no difference to this. After a short introduction to randomized norms and geometry in Banach spaces we study the Rademacher maximal function on Euclidean spaces. The requirements on the type and cotype are considered, providing examples of spaces without RMF. L^p-spaces are shown to have RMF not only for p greater or equal to 2 (when it is trivial) but also for 1 < p < 2. A dyadic version of Carleson's embedding theorem is proven for scalar- and operator-valued functions. As the analysis with dyadic cubes can be generalized to filtrations on sigma-finite measure spaces, we consider the Rademacher maximal function in this case as well. It turns out that the RMF-property is independent of the filtration and the underlying measure space and that it is enough to consider very simple ones known as Haar filtrations. Scalar- and operator-valued analogues of Carleson's embedding theorem are also provided. With the RMF-property proven independent of the underlying measure space, we can use probabilistic notions and formulate it for martingales. Following a similar result for UMD-spaces, a weak type inequality is shown to be (necessary and) sufficient for the RMF-property. The RMF-property is also studied using concave functions giving yet another proof of its independence from various parameters.

Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Macroseismology as a Component of Seismicity Assessments in an Intraplate Region: Studies of Northern Europe with Emphasis on Finland

This work focuses on the role of macroseismology in the assessment of seismicity and probabilistic seismic hazard in Northern Europe. The main type of data under consideration is a set of macroseismic observations available for a given earthquake. The macroseismic questionnaires used to collect earthquake observations from local residents since the late 1800s constitute a special part of the seismological heritage in the region. Information of the earthquakes felt on the coasts of the Gulf of Bothnia between 31 March and 2 April 1883 and on 28 July 1888 was retrieved from the contemporary Finnish and Swedish newspapers, while the earthquake of 4 November 1898 GMT is an example of an early systematic macroseismic survey in the region. A data set of more than 1200 macroseismic questionnaires is available for the earthquake in Central Finland on 16 November 1931. Basic macroseismic investigations including preparation of new intensity data point (IDP) maps were conducted for these earthquakes. Previously disregarded usable observations were found in the press. The improved collection of IDPs of the 1888 earthquake shows that this event was a rare occurrence in the area. In contrast to earlier notions it was felt on both sides of the Gulf of Bothnia. The data on the earthquake of 4 November 1898 GMT were augmented with historical background information discovered in various archives and libraries. This earthquake was of some concern to the authorities, because extra fire inspections were conducted in three towns at least, i.e. Tornio, Haparanda and Piteå, located in the centre of the area of perceptibility. This event posed the indirect hazard of fire, although its magnitude around 4.6 was minor on the global scale. The distribution of slightly damaging intensities was larger than previously outlined. This may have resulted from the amplification of the ground shaking in the soft soil of the coast and river valleys where most of the population was found. The large data set of the 1931 earthquake provided an opportunity to apply statistical methods and assess methodologies that can be used when dealing with macroseismic intensity. It was evaluated using correspondence analysis. Different approaches such as gridding were tested to estimate the macroseismic field from the intensity values distributed irregularly in space. In general, the characteristics of intensity warrant careful consideration. A more pervasive perception of intensity as an ordinal quantity affected by uncertainties is advocated. A parametric earthquake catalogue comprising entries from both the macroseismic and instrumental era was used for probabilistic seismic hazard assessment. The parametric-historic methodology was applied to estimate seismic hazard at a given site in Finland and to prepare a seismic hazard map for Northern Europe. The interpretation of these results is an important issue, because the recurrence times of damaging earthquakes may well exceed thousands of years in an intraplate setting such as Northern Europe. This application may therefore be seen as an example of short-term hazard assessment.

Communicative and Anticipatory Decision-making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.