We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems.

Automating the learning of causal models from sample data is a key step toward incorporating machine learning into decisionmaking and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate re ections of the original models and compare favorably with those of TETRAD II (Spirtes et al. 1994) even when it is supplied with prior temporal information and MML is not. 1

Brain imaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide a unique opportunity to study the neurobiology of human memory. Since these methods can measure most of the brain, it is possible to examine the operations of large-scale neural systems and their relation to cognition. Two neuroimaging studies, one concerning working memory and the other episodic memory retrieval, serve as examples of application of two analytic methods that are optimized for the quantification of neural systems, structural equation modeling and partial least squares. Structural equation modeling was used to explore shifting prefrontal and limbic interactions from the right to the left hemisphere in a delayed match-to-sample task for faces. A feature of the functional network for short delays was strong right hemisphere interactions between hippocampus, inferior prefrontal, and anterior cingulate cortices. At longer delays, these same three areas were strongly linked, but in the left hemisphere, which was interpreted as reflecting change in task strategy from perceptual to elaborate encoding with increasing delay. The primary manipulation in the memory retrieval study was different levels of retrieval success. Partial least squares was used to determine whether the image-wide pattern of covariances of Brodmann areas 10 and 45/47 in right prefrontal cortex (RPFC) and the left hippocampus (LGH) could be mapped on to retrieval levels. Area 10 and LGH showed an opposite pattern of functional connectivity with a large expanse of bilateral limbic cortices that was equivalent for all levels of retrieval as well as the baseline task. However, only during high retrieval area 45/47 was included in this pattern. The results suggest that activ...

this paper we present data of large samples of twin families, with an equal number of girls and boys. The well-known gender difference with boys displaying more OA and AP was observed at each age. Even at the age of 3, boys display more OA problems than girls. Clinical studies have indicated that severe problem behavior can be identified in very young children (see for review, Campbell, 1995; Keenan & Wakschlag, 2000; Shaw, Owens, Giovannelli, & Winslow, 2001) and that the onset of ADHD is during the pre-school period (Barkley, Fisher, Edelbrock, & Smallish, 1990; Table 6 Top part includes percentages of total variances (diagonal) and covariances (off-diagonal) explained by additive genetic, genetic dominance, and unique environmental components based on best fitting models. Percentages for boys and girls are reported below and above diagonal, respectively. Lower part includes correlations calculated for additive genetic, genetic dominance, and unique environmental sources of variance between different ages. Correlations for boys and girls are reported below and above diagonal, respectively Relative proportions of variance and covariance BoysnGirls A% D% E% OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 50n41 73 79 75 22n33 17 13 14 28n26 10 8 11 AP 7 59 33n57 50 53 31 39n16 31 28 10 28n27 19 19 AP 10 86 31 41n48 47 6 51 31n25 32 8 18 28n27 21 AP 12 71 24 31 40n54 16 55 45 30n18 13 21 24 30n28 Correlations between different ages BoysnGirls ADE OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 1.00 .60 .66 .57 1.00 .30 .16 .20 1.00 .15 .12 .14 AP 7 .57 1.00 .62 .57 .41 1.00 .99 1.00 .15 1.00 .46 .41 AP 10 .68 .56 1.00 .61 .08 .94 1.00 1.00 .11 .42 1.00 .50 AP 12 .49 .42 .53 1.00 .20 .98 .99 1.00 .14 .45 .58 1.00 ...

INTRODUCTION The structure of individual differences in cognitive abilities during development has received considerable attention in cognitive developmental theory (Schaie, 1994; Vernon, 1976). One important hypothesis, dating back to Garrett (1946; see also Carroll, 1993; Reinert, 1970; Wohlwill, 1973), states that cognitive abilities become increasingly more differentiated during development. In operational terms, this means that the intercorrelations among psychometric measures of ability decrease during normal cognitive development in children. So far, support for this hypothesis has been poor. In an early review of about 60 factor analytic studies, Reinert (1970) suggested that a trend toward increased differentiation was present. However, this conclusion was based on the selection of studies that actually reported a change. In a more recent review, Carroll (1993) failed to find clear evidence for the differentiation of abilities. More recent cross-sectional studies of the cha

This study examined the direction and time-frame of relationships between perceived self-efficacy in classroom management and the three dimensions of burnout among 243 secondary school teachers. Structural equation modeling (SEM) analyses indicated that perceived self-efficacy had a longitudinal effect on depersonalization and a synchronous effect on personal accomplishment. However, the direction was reversed for the relationship between perceived self-efficacy and emotional exhaustion; the time frame was synchronous. It was concluded that perceived self-efficacy in classroom management must be taken into consideration when devising interventions both to prevent and to treat burnout among secondary school teachers.

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This paper presents a procedure aimed at recognizing environmental sounds for surveillance and security applications. We propose to apply One-Class Support Vector Machines (1-SVMs) together with a sophisticated dissimilarity measure as a discriminative framework in order to address audio classification, and hence, sound recognition. We illustrate the performance of this method on an audio database, which consists of above 1,000 sounds belonging to 9 classes. Additionally, the use of a set of state-of-the-art audio features is studied. Additionally, we introduce a set of novel features obtained by combining elementary features. Experimental results are presented and show the superiority of this novel sound recognition method. We show that the 1-SVM clearly overperforms the conventional HMM-based system and we emphasize that the largest improvement is achieved when the system is fed by a set of features that comprises wavelet coefficients.

Summary. Although factor analytic models have proven useful for covariance structure modeling and dimensionality reduction in a wide variety of applications, a challenging prob-lem is uncertainty in the number of latent factors. This article proposes an efficient Bayesian approach for model selection and averaging in hierarchical models having one or more factor analytic components. In particular, the approach relies on a method for embedding each of the smaller models within the largest possible model. Bayesian computation can proceed within the largest model, while moving between sub-models based on posterior model prob-abilities. The approach represents a type of parameter expansion, as one always samples within an encompassing model, incorporating extra parameters and latent variables when a smaller model is true. This results in a highly efficient stochastic search factor selection algo-rithm (SSFS) for identifying good factor models and performing model-averaged inferences. The approach is illustrated using simulated examples and a toxicology application.

The completion of this dissertation is the end of one very educational era in my life. I am full of gratitude for many inspiring moments with interesting people during these years. I would like to thank the following people for their contribution to the production of this research. First of all, Professor Teija Laitinen, my supervisor, who guided me into the academic world. Our discussions gave me a lot of encouragement that was needed to conduct this research. I will always appreciate the freedom she gave me to take my own way and her great faith in my skills to survive. Profound gratitude also goes to the official pre-examiners Professor Norman Macintosh and Professor Lars Hassel for their review and very thoughtful feedback on the manuscript. The final version of this dissertation has benefited immensely from their efforts. Many professors gave valuable comments on my manuscript during the research process. Erkki K. Laitinen, Robert Chenhall, David Cooper, Tom Groot, Jan Mouritsen and Michael Shields are to be thanked. I am thankful for Adebayo Agbejule for guiding me into the interesting world of structural equation modeling. Professor Esko Leskinen is gratefully acknowledged for his valuable suggestions and guidance on the quantitative analysis whenever it was needed. The expertise of Professor Ilkka Virtanen, Professor Seppo Pynnönen, Pentti Suomela, Hannu Hirvonen and Tommi Lehtonen in various fields likewise improved this research. There are many professors, colleagues, administrative staff and friends at the University of Vaasa who supported me during the dissertation process. I appreciate their help and effort to create an inspiring research environment. I specially wish to thank Professor