In statistics , a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models should not be confused with models for compositional data , i.
Gaussian Mixture Models
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For sustainable and resilient infrastructure. In March took place the kickoff meeting of the ClimaBridge Project — Impact of climate change on the structural health of bridges. This award was established to recognize authors of outstanding papers presented at IMAC. Papers are judged for their value as contributions to existing knowledge of computer vision and optical techniques and primarily with respect to their value as an original contribution to the subject matter. The SHM is posed on a statistical pattern recognition paradigm, where machine learning algorithms are essential to learn or to model the structural behavior from the experience past data , following the same principle of the human brain, in order to analyze Big Data and to perform pattern recognition for damage identification. This concept is rooted in the Artificial Intelligence. In order to balance the general concept and the applicability of SHM, this course gives some insight about the theory and application of some techniques, such as Gaussian mixture models, kernel principal component analysis, deep learning, regression models, and chaotic systems.
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Adrian Raftery: Model-Based Clustering Research
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We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task.