Search for dissertations about: "Latent Variable Models"
Showing result 16 - 20 of 44 swedish dissertations containing the words Latent Variable Models.
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16. Towards a flexible statistical modelling by latent factors for evaluation of simulated responses to climate forcings
Abstract : In this thesis, using the principles of confirmatory factor analysis (CFA) and the cause-effect concept associated with structural equation modelling (SEM), a new flexible statistical framework for evaluation of climate model simulations against observational data is suggested. The design of the framework also makes it possible to investigate the magnitude of the influence of different forcings on the temperature as well as to investigate a general causal latent structure of temperature data. READ MORE
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17. Spatial Mixture Models with Applications in Medical Imaging and Spatial Point Processes
Abstract : Finite mixture models have proven to be a great tool for both modeling non-standard probability distributions and for classification problems (using the latent variable interpretation). In this thesis we are building spatial models by incorporating spatially dependent categorical latent random fields in a hierarchical manner similar to that of finite mixture models. READ MORE
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18. Cognitive abilities - aspects of structure, process and measurement
Abstract : The overall purpose of the thesis is to describe the development of the Swedish system of measuring cognitive abilities applied at enlistment of conscripts. The Enlistment Battery has been used for more than fifty years to classify 18-year old men into military positions for their compulsory service. READ MORE
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19. Topics on Generative Models in Machine Learning
Abstract : Latent variable models have been extensively studied within the field of machine learning in recent years. Especially in combination with neural networks and training through back propagation, they have proven successful for a variety of tasks; notably sample gener- ation, clustering, disentanglement and interpolation. READ MORE
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20. Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision
Abstract : Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. READ MORE