Integrating varve and tree-ring time series for southern California climate reconstruction: a 20th century outlook [abstract]

EXTRACT (SEE PDF FOR FULL ABSTRACT): Our objective is to combine terrestrial and oceanic records for reconstructing West Coast climate. Tree rings and marine laminated sediments provide high-resolution, accurately dated proxy data on the variability of climate and on the productivity of the ocean and have been used to reconstruct precipitation, temperature, sea level pressure, primary productivity, and other large-scale parameters. We present here the latest Santa Barbara basin varve chronology for the twentieth century as well as a newly developed tree-ring chronology for Torrey pine.

Tree-Structured Stick Breaking Processes for Hierarchical Data

Many data are naturally modeled by an unobserved hierarchical structure. In this paper we propose a flexible nonparametric prior over unknown data hierarchies. The approach uses nested stick-breaking processes to allow for trees of unbounded width and depth, where data can live at any node and are infinitely exchangeable. One can view our model as providing infinite mixtures where the components have a dependency structure corresponding to an evolutionary diffusion down a tree. By using a stick-breaking approach, we can apply Markov chain Monte Carlo methods based on slice sampling to perform Bayesian inference and simulate from the posterior distribution on trees. We apply our method to hierarchical clustering of images and topic modeling of text data.

Decision tree-based context clustering based on cross validation and hierarchical priors

The standard, ad-hoc stopping criteria used in decision tree-based context clustering are known to be sub-optimal and require parameters to be tuned. This paper proposes a new approach for decision tree-based context clustering based on cross validation and hierarchical priors. Combination of cross validation and hierarchical priors within decision tree-based context clustering offers better model selection and more robust parameter estimation than conventional approaches, with no tuning parameters. Experimental results on HMM-based speech synthesis show that the proposed approach achieved significant improvements in naturalness of synthesized speech over the conventional approaches. © 2011 IEEE.

A hierarchical approach coupled with coarse DEM information for improving the efficiency and accuracy of forest mapping over very rugged terrains

Forest mapping over mountainous terrains is difficult because of high relief Although digital elevation models (DEMs) are often useful to improve mapping accuracy, high quality DEMs are seldom available over large areas, especially in developing countries