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.

A Touch of Red : Archaeological and Ethnographic Approaches to Interpreting Finnish Rock Paintings

Approximately 125 prehistoric rock paintings have been found in the modern territory of Finland. The paintings were done with red ochre and are almost without exception located on steep lakeshore cliffs associated with ancient water routes. Most of the sites are found in the central and eastern parts of the country, especially on the shores of Lakes Päijänne and Saimaa. Using shore displacement chronology, the art has been dated to ca. 5000 – 1500 BC. It was thus created mainly during the Stone Age and can be associated with the so-called ‘Comb Ware’ cultures of the Subneolithic period. The range of motifs is rather limited, consisting mainly of schematic depictions of stick-figure humans, elks, boats, handprints and geometric signs. Few paintings include any evidence of narrative scenes, making their interpretation a rather difficult task. In Finnish archaeological literature, the paintings have traditionally been associated with ’sympathetic’ hunting magic, or the belief that the ritual shooting of the painted animals would increase hunting luck. Some writers have also suggested totemistic and shamanistic readings of the art. This dissertation is a critical review of the interpretations offered of Finnish rock art and an exploration of the potentials of archaeological and ethnographic research in increasing our knowledge of its meaning. Methods used include ’formal’ approaches such as archaeological excavation, landscape analysis and the application of neuropsychological research to the study of rock art, as well as ethnographically ’informed’ approaches that make use of Saami and Baltic Finnish ethnohistorical sources in interpretation. In conclusion, it is argued that although North European hunter-gatherer rock art is often thought to lie beyond the reach of ‘informed’ knowledge, the exceptional continuity of prehistoric settlement in Finland validates the informed approach in the interpretation of Finnish rock paintings. The art can be confidently associated with shamanism of the kind still practiced by the Saami of Northern Fennoscandia in the historical period. Evidence of similar shamanistic practices, concepts and cosmology are also found in traditional Finnish-Karelian epic poetry. Previous readings of the art based on ‘hunting magic’ and totemism are rejected. Most of the paintings appear to depict experiences of falling into a trance, of shamanic metamorphosis and trance journeys, and of ‘spirit helper’ beings comparable to those employed by the Saami shaman (noaidi). As demonstrated by the results of an excavation at the rock painting of Valkeisaari, the painted cliffs themselves find a close parallel in the Saami cult of the 'sieidi', or sacred cliffs and boulders worshipped as expressing a supernatural power. Like the Saami, the prehistoric inhabitants of the Finnish Lake Region seem to have believed that certain cliffs were ’alive’ and inhabited by the spirit helpers of the shaman. The rock paintings can thus be associated with shamanic vision quests, and the making of ‘art’ with an effort to socialize the other members of the community, especially the ritual specialists, with trance visions. However, the paintings were not merely to be looked at. The red ochre handprints pressed on images of elks, as well as the fact that many paintings appear ’smeared’, indicate that they were also to be touched – perhaps in order to tap into the supernatural potency inherent in the cliff and in the paintings of spirit animals.

Exploring the experiences and conceptions of good teaching in higher education : Development of a questionnaire for assessing students' approaches to learning and experiences of the teaching-learning environment

The aim of this dissertation was to adapt a questionnaire for assessing students’ approaches to learning and their experiences of the teaching-learning environment. The aim was to explore the validity of the modified Experiences of Teaching and Learning Questionnaire (ETLQ) by examining how the instruments measure the underlying dimensions of student experiences and their learning. The focus was on the relation between students’ experiences of their teaching-learning environment and their approaches to learning. Moreover, the relation between students’ experiences and students’ and teachers’ conceptions of good teaching was examined. In Study I the focus was on the use of the ETLQ in two different contexts: Finnish and British. The study aimed to explore the similarities and differences between the factor structures that emerged from both data sets. The results showed that the factor structures concerning students’ experiences of their teaching-learning environment and their approaches to learning were highly similar in the two contexts. Study I also examined how students’ experiences of the teaching-learning environment are related to their approaches to learning in the two contexts. The results showed that students’ positive experiences of their teaching-learning environment were positively related to their deep approach to learning and negatively to the surface approach to learning in both the Finnish and British data sets. This result was replicated in Study II, which examined the relation between approaches to learning and experiences of the teaching-learning environment on a group level. Furthermore, Study II aimed to explore students’ approaches to learning and their experiences of the teaching-learning environment in different disciplines. The results showed that the deep approach to learning was more common in the soft sciences than in the hard sciences. In Study III, students’ conceptions of good teaching were explored by using qualitative methods, more precisely, by open-ended questions. The aim was to examine students’ conceptions, disciplinary differences and their relation to students’ approaches to learning. The focus was on three disciplines, which differed in terms of students’ experiences of their teaching-learning environment. The results showed that students’ conceptions of good teaching were in line with the theory of good teaching and there were disciplinary differences in their conceptions. Study IV examined university teachers’ conceptions of good teaching, which corresponded to the learning-focused approach to teaching. Furthermore, another aim in this doctoral dissertation was to compare the students’ and teachers’ conceptions of good teaching, the results of which showed that these conceptions appear to have similarities. The four studies indicated that the ETLQ appears to be a sufficiently robust measurement instrument in different contexts. Moreover, its strength is its ability to be at the same time a valid research instrument and a practical tool for enhancing the quality of students’ learning. In addition, the four studies emphasise that in order to enhance teaching and learning in higher education, various perspectives have to be taken into account. This study sheds light on the interaction between students’ approaches to learning, their conceptions of good teaching, their experiences of the teaching-learning environment, and finally, the disciplinary culture.

Teaching in Higher Education : From Content-focused to Learning-focused Approaches to Teaching

The aim of this dissertation was to explore teaching in higher education from the teachers’ perspective. Two of the four studies analysed the effect of pedagogical training on approaches to teaching and on self-efficacy beliefs of teachers on teaching. Of these two studies, Study I analysed the effect of pedagogical training by applying a cross-sectional setting. The results showed that short training made teachers less student-centred and decreased their self-efficacy beliefs, as reported by the teachers themselves. However, more constant training enhanced the adoption of a student-centred approach to teaching and increased the self-efficacy beliefs of teachers as well. The teacher-focused approach to teaching was more resistant to change. Study II, on the other hand, applied a longitudinal setting. The results implied that among teachers who had not acquired more pedagogical training after Study II there were no changes in the student-focused approach scale between the measurements. However, teachers who had participated in further pedagogical training scored significantly higher on the scale measuring the student-focused approach to teaching. There were positive changes in the self-efficacy beliefs of teachers among teachers who had not participated in further training as well as among those who had. However, the analysis revealed that those teachers had the least teaching experience. Again, the teacher-focused approach was more resistant to change. Study III analysed approaches to teaching qualitatively by using a large and multidisciplinary sample in order to capture the variation in descriptions of teaching. Two broad categories of description were found: the learning-focused and the content-focused approach to teaching. The results implied that the purpose of teaching separates the two categories. In addition, the study aimed to identify different aspects of teaching in the higher-education context. Ten aspects of teaching were identified. While Study III explored teaching on a general level, Study IV analysed teaching on an individual level. The aim was to explore consonance and dissonance in the kinds of combinations of approaches to teaching university teachers adopt. The results showed that some teachers were clearly and systematically either learning- or content-focused. On the other hand, profiles of some teachers consisted of combinations of learning- and content-focused approaches or conceptions making their profiles dissonant. Three types of dissonance were identified. The four studies indicated that pedagogical training organised for university teachers is needed in order to enhance the development of their teaching. The results implied that the shift from content-focused or dissonant profiles towards consonant learning-focused profiles is a slow process and that teachers’ conceptions of teaching have to be addressed first in order to promote learning-focused teaching.

Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

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.

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.

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.

Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.