Hierarchical Mode Association Clustering / Mode Association Clustering (HMAC, MAC) google
Mode association clustering (MAC) can be conducted either hierarchically or at one level. MAC is similar to mixture model based clustering in the sense of characterizing clusters by smooth densities. However, MAC requires no model fitting and uses a nonparametric kernel density estimation. The density of a cluster is not restricted to be parametric, for instance, Gaussian, but ensures uni-modality. The algorithm seems to combine the complementary merits of bottom-up clustering such as linkage and topdown clustering such as mixture modeling and k-means. It also tends to be robust against non-Gaussian shaped clusters. …

Stata google
Stata is a complete, integrated statistical software package that provides everything you need for data analysis, data management, and graphics. With both a point-and-click interface and a powerful, intuitive command syntax, Stata is fast, accurate, and easy to use. All analyses can be reproduced and documented for publication and review. Version control ensures statistical programs will continue to produce the same results no matter when you wrote them. …

Online Maximum a Posterior Estimation (OPE) google
One of the core problems in statistical models is the estimation of a posterior distribution. For topic models, the problem of posterior inference for individual texts is particularly important, especially when dealing with data streams, but is often intractable in the worst case. As a consequence, existing methods for posterior inference are approximate and do not have any guarantee on neither quality nor convergence rate. In this paper, we introduce a provably fast algorithm, namely Online Maximum a Posterior Estimation (OPE), for posterior inference in topic models. OPE has more attractive properties than existing inference approaches, including theoretical guarantees on quality and fast convergence rate. The discussions about OPE are very general and hence can be easily employed in a wide class of probabilistic models. Finally, we employ OPE to design three novel methods for learning Latent Dirichlet allocation from text streams or large corpora. Extensive experiments demonstrate some superior behaviors of OPE and of our new learning methods. …

Advertisements