Simple approximate MAP inference for Dirichlet processes mixtures

Mathematics

• Maximum a Posteriori 82%
• Dirichlet Process 79%
• Mixture Model 40%
• Bayesian Nonparametrics 31%
• Gibbs Sampling 30%
• Process Model 28%
• Asymptotic Variance 26%
• Clustering 24%
• Resources 23%
• Probabilistic Inference 19%
• Digital Signal Processing 18%
• Gaussian Mixture 17%
• Mixed Effects 16%
• Hardware 15%
• Performance 15%
• Progression 14%
• Cross-validation 14%
• Nonparametric Model 14%
• Variational Approach 13%
• Asymptotic Methods 13%
• Markov Model 13%
• Health 13%
• Random Effects 12%
• Statistical Model 12%
• Computational Complexity 11%
• Model-based 11%
• Likelihood 11%
• Closed-form 10%
• Regression Model 10%
• Modeling 8%
• Range of data 7%
• Demonstrate 7%
• Standards 7%
• Model 4%

Business & Economics

• Dirichlet Process 100%
• Mixture Model 66%
• Inference 52%
• Asymptotic Variance 52%
• Gibbs Sampling 37%
• Clustering 23%
• Hidden Markov Model (HMM) 20%
• Cross-validation 20%
• Statistical Model 17%
• Population Health 16%
• Resources 16%
• Computational Complexity 15%
• Random Effects 13%
• Sampling 13%
• Hardware 12%
• Progression 12%
• Regression Model 10%
• Premium 10%
• Performance 9%
• Modeling 7%