Particle Gibbs for Bayesian Additive Regression Trees

Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilistic predictions with measures of uncertainty, the Bayesian additive regression trees (BART) model, introduced by Chipman et al. (2010), is increasingly popular. As data sets have grown in size, however, the standard Metropolis-Hastings algorithms used to perform inference in BART are proving inadequate. In particular, these Markov chains make local changes to the trees and suffer from slow mixing when the data are high-dimensional or the best fitting trees are more than a few layers deep. We present a novel sampler for BART based on the Particle Gibbs (PG) algorithm (Andrieu et al., 2010) and a top-down particle filtering algorithm for Bayesian decision trees (Lakshminarayanan et al., 2013). Rather than making local changes to individual trees, the PG sampler proposes a complete tree to fit the residual. Experiments show that the PG sampler outperforms existing samplers in many settings

Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale non-parametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.Comment: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. JMLR: W&CP volume 5

Counting the Invisible Working Hands in India

This paper enquires into the problem faced by migrant labors in the informal sector in India It argues for the registration and tracking of migrants particularly interstate migrants as they constitute a significant proportion of the labour force The mobility of labor is the natural mechanism for infusing inclusive and faster growth across the globe and tracking the poor migrants would lead to investment in human and social capital paving the way for integrating poverty alleviation policies and development strategies for overall better outcome

A HISTORICAL APPROACH FOR UNDERSTANDING AYURVEDA

Ayurveda is the traditional system of Indian medicine and being historical, linguistic, religious, philosophical importance of Ayurveda, it is very necessary to do critical study of Ayurveda according to ancient Indias history, Prakrita, Pali (Magdhi), Ardhamagdhi languages, religious and philosophical point of view. It is prejudice to compare Ayurveda with the modern science. First, we should try to know what is Ayurveda? It is considered as science of life but what is the meaning of science according to Indian philosophy? Ayurveda is considered as the philosophy as well as clinical science also. The two main distinctions of Indian philosophy – the believer (i.e. Sankhya, Yog, Nyaya, Vaisheshika, Purva Mimansa, Uttar Mimansa) and the atheist (i.e. Charvaka, Jain and Baudha) philosophy. Unless and until we have the deep knowledge of Indian philosophical sciences i.e. Darshan Shastras (Aastika and Naastika), we wont be able to understand Ayurveda properly. Therefore, it is the need of time to do critical study of Darshan Shastras (Aastika and Naastika) and it is to be noted that with the help of Sanskrita language alone we wont be able to achieve our goal; it is very much important to have the knowledge of languages such as Prakrit, Pali which is also known as Magadhi and Ardhamagdhi, then only we could be able to understand the proper meanings of Darshan Shastras (Aastika and Naastika); Buddhist literature is available in the Pali language which is also known as Magdhi; it was the dialect of ancient north India, and Ardhamagdhi is the language in which Jain literature is available. This research paper is an attempt to review the Ayurveda literature regarding historical facts and what are the means so that we could able to understand Ayurveda appropriately.

Radiative stability of neutrino-mass textures

Neutrino-mass textures proposed at high-scales are known to be unstable against radiative corrections especially for nearly degenerate eigen values. Within the renormalization group constraints we find a mechanism in a class of gauge theories which guarantees reproduction of any high-scale texture at low energies with radiative stability. We also show how the mechanism explains solar and atmospheric neutrino anomalies through the bimaximal texture at high scale.Comment: 4 pages REVTEX, 1 Postscript fi