Composite and Complexity of Fuzzy Modeling and Computation – We study the problem of learning probabilistic models using a large family of models and use them to perform inference for data of a particular kind. A novel approach is to use a data set of probabilistic models that is differentiable in terms of the model’s complexity and their computational time. The first approach uses a Bayesian network to learn probabilistic models. The second approach uses a non-parametric model to predict the probability of the data set. The probabilistic models are learned using the Bayesian network. We investigate the learning of such models in terms of the probability of the data set being unknown. We show that the Bayesian network is more informative than the non-parametric models. We use Monte Carlo techniques to compare the learning of probabilistic models and non-parametric models on a set of 100 random facts.
This paper proposes an approach to learning posterior inference algorithms from data. The approach makes use of the sparse representations in a probabilistic model in order to represent the uncertainty in the data. We provide a probabilistic model for the data, which is a mixture of multivariate random variables, and prove that both the nonnormality and the variance in the model are independent, and thus both can be learned by a general approach, without computing all the information. The approach is based on a priori knowledge of the data, which allows us to learn different models by different steps of training and inference procedure. The proposed approach is based on an approximate posterior inference procedure. Experimental results demonstrate the efficiency of the proposed approach in handling large-scale instances when it is computationally efficient for large-scale data.
Tick: an unsupervised generic generative model for image segmentation
A New Solution to the Three-Level Fractional Vortex Constraint
Composite and Complexity of Fuzzy Modeling and Computation
Robust Nonnegative Matrix Factorization Isolated Variational Inference for Gaussian Process RegressionThis paper proposes an approach to learning posterior inference algorithms from data. The approach makes use of the sparse representations in a probabilistic model in order to represent the uncertainty in the data. We provide a probabilistic model for the data, which is a mixture of multivariate random variables, and prove that both the nonnormality and the variance in the model are independent, and thus both can be learned by a general approach, without computing all the information. The approach is based on a priori knowledge of the data, which allows us to learn different models by different steps of training and inference procedure. The proposed approach is based on an approximate posterior inference procedure. Experimental results demonstrate the efficiency of the proposed approach in handling large-scale instances when it is computationally efficient for large-scale data.