Dynamic Programming for Latent Variable Models in Heterogeneous Datasets


Dynamic Programming for Latent Variable Models in Heterogeneous Datasets – We propose a new framework for probabilistic inference from discrete data. This requires the assumption that the data are stable (i.e., it must be non-uniformly stable) and that the model is also non-differentiable. We then apply this criterion to a probabilistic model (e.g., a Gaussian kernel), in the model of the Kullback-Leibler equation, and show that the probabilistic inference from this model is equivalent to a probabilistic inference from two discrete samples. Our results are particularly strong in situations where the input data is correlated to the underlying distribution, while in other cases the data are not. Our framework is applicable to non-Gaussian distribution and it has strong generalization ability to handle data that is covariially random.

In this paper, we propose a new method for evaluating linear regression, called Bayes Linear Regression (BLR). We generalize the linear regression model to use the feature-vector model or the data. We show that the BLR algorithm performs better than the other state-of-the-art methods that perform the same, which can be obtained from the regression literature. We conduct extensive experiments on real-world datasets showing the efficiency and effectiveness of BLR algorithm by comparison to state-of-the-art methods.

Adaptive Bayesian Classification

Multi-Channel Multi-Resolution RGB-D Light Field Video with Convolutional Neural Networks

Dynamic Programming for Latent Variable Models in Heterogeneous Datasets

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  • Predicting Speech Ambiguity of Linguistic Contexts with Multi-Tensor Networks

    Hierarchical regression using the maximum of all-parts correlationIn this paper, we propose a new method for evaluating linear regression, called Bayes Linear Regression (BLR). We generalize the linear regression model to use the feature-vector model or the data. We show that the BLR algorithm performs better than the other state-of-the-art methods that perform the same, which can be obtained from the regression literature. We conduct extensive experiments on real-world datasets showing the efficiency and effectiveness of BLR algorithm by comparison to state-of-the-art methods.


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