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Unbiased Estimating Equation on Inverse Divergence and Its Conditions
For the loss function defined by the monotonically increasing function $f$ and inverse divergence, the conditions for the statistical model and function $f$ under which the estimating equation is unbiased are clarified.
Unified Likelihood Ratio Estimation for High- to Zero-frequency N-grams
A naive estimation approach that uses only $N$-gram frequencies is sensitive to low-frequency (rare) $N$-grams and not applicable to zero-frequency (unobserved) $N$-grams; these are known as the low- and zero-frequency problems, respectively.
Unbiased Estimation Equation under $f$-Separable Bregman Distortion Measures
We discuss unbiased estimation equations in a class of objective function using a monotonically increasing function $f$ and Bregman divergence.
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis.
Generalized Dirichlet-process-means for $f$-separable distortion measures
Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters.
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and images, where an object is expressed as a mixture of latent topics.
Bayesian Properties of Normalized Maximum Likelihood and its Fast Computation
The normalized maximized likelihood (NML) provides the minimax regret solution in universal data compression, gambling, and prediction, and it plays an essential role in the minimum description length (MDL) method of statistical modeling and estimation.
