New method calculates DMN log-likelihood faster.
problem Precise and fast computation of DMN log-likelihood.
method Derived a closed form expression using gamma function properties.
result Closed form calculation is faster with same accuracy.
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20 results for “Dirichlet-Multinomial”
Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomialstat.ML
Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.
problem Poor parameter estimates and accuracy in multinomial and Dirichlet multinomial distributions when assumptions are violated.
method Introduces Beta-Liouville multinomial distribution and efficient estimation methods.
result Beta-Liouville multinomial outperforms standard distributions on two out of four datasets.
New Gamma-Poisson model improves topic selection for short text.
problem Topic modelling for short text using Poisson distribution.
method Gamma-Poisson mixture model with collapsed Gibbs sampler.
result Gamma-Poisson model selects more accurate number of topics.
Deviance-style normalization for sparse, jointly overdispersed count matrices
problem Jointly overdispersed count matrices
method Dirichlet-multinomial deviance residualization
result Preserves exact sparsity, evaluates in constant time, recovers multinomial residual
Paper uses subjective logic to estimate uncertainty in multi-armed bandit problems.
problem Estimating uncertainty in multi-armed bandit problems.
method Formalism of subjective logic applied to multi-armed bandits, proposing new algorithms.
result Subjective logic quantities enable useful assessment of uncertainty.
This work embeds annotations into a multidimensional space to measure classification difficulty.
problem Uncertainty in machine learning models during annotation phase.
method Develops a Bayesian Dirichlet-Multinomial framework to embed annotations and uses stochastic Expectation Maximization with MCMC.
result Embeddings reflect semantic similarities of original classes, aiding in measuring classification difficulty.
Given a collection of categorical data, we want to find the parameters of a Dirichlet distribution which maximizes the likelihood of that data. Newton's method is typically used for this purpose but current implementations require reading through the entire dataset on each iteration. In this paper, we propose a modific…
In this technical report, we present jLDADMM---an easy-to-use Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the one-topic-per-document Dirichlet Multinomi…
The study analyzes when Bayesian averaging over decision trees is reliable.
problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.
Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negati…
Root Cause Analysis for Anomalies is challenging because of the trade-off between the accuracy and its explanatory friendliness, required for industrial applications. In this paper we propose a framework for simple and friendly RCA within the Bayesian regime under certain restrictions (that Hessian at the mode is diago…
Recent advances in Bayesian reinforcement learning (BRL) have shown that Bayes-optimality is theoretically achievable by modeling the environment's latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. In self-interested multi-agent environments, the transition dynamics are mainly controlled by the other agent'…
Many practical modeling problems involve discrete data that are best represented as draws from multinomial or categorical distributions. For example, nucleotides in a DNA sequence, children's names in a given state and year, and text documents are all commonly modeled with multinomial distributions. In all of these cas…
Bayesian models predict Collatz stopping times with high accuracy.
problem Predicting the total stopping time of Collatz sequences.
method Developed two complementary models: a hierarchical Negative Binomial regression and a mechanistic generative approximation.
result Bayesian models outperform generative approximations in predicting Collatz stopping times.
nnLDA combines neural and probabilistic methods for better topic modeling with side information.
problem Lack of integration of auxiliary information in traditional topic models.
method nnLDA integrates side information through a neural prior mechanism, optimizing both neural and probabilistic components.
result nnLDA outperforms traditional models in topic coherence, perplexity, and classification.
A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capturing the tendency for a covariate present in a sample to both repeat itself and excite related ones…
Unified framework for fair decision-making across diverse groups.
problem Statistical brittleness in fairness testing for small subgroups.
method Size-adaptive hypothesis testing framework.
result Validated approach for interpretable, statistically rigorous decisions.
A Sufficient Statistics Construction of Bayesian Nonparametric Exponential Family Conjugate Modelscs.LG
Conjugate pairs of distributions over infinite dimensional spaces are prominent in statistical learning theory, particularly due to the widespread adoption of Bayesian nonparametric methodologies for a host of models and applications. Much of the existing literature in the learning community focuses on processes posses…
Geometric QHD tests improve hub detection in correlated data.
problem Detecting hubs in correlated data with evolving correlations.
method Geometric QHD tests combining QCD and QHD, clustering.
result Improved hub detection in correlated data.
New models automate support group formation in online health communities.
problem Challenges in traditional support group formation methods for scalability, static categorization, and insufficient personalization.
method Two novel machine learning models: gDMR and gSTM, integrating user content, demographics, and network data.
result Models outperform baselines in predictive accuracy, semantic coherence, and internal group consistency.