International Business Machines Corporation

Ashutosh Garg

Shivakumar Vaithyanathan

Sreeram Viswanath Balakrishnan

Huaiyu Zhu

Jayram S Thathachar

A system, method and program storage device implementing a method for modeling a data generating process, wherein the modeling comprises observing a data sequence comprising irregularly sampled data, obtaining an observation sequence based on the observed data sequence, assigning a time index sequence to the data sequence, obtaining a hidden state sequence of the data sequence, and decoding the data sequence based on a combination of the time index sequence and the hidden state sequence to model the data sequence. The method further comprises assigning a probability distribution over time stamp values of the observation sequence, wherein the decoding comprises using a Hidden Markov Model. The method further comprises using an expectation maximization methodology to learn the Hidden Markov Model.

Asynchronous Hidden Markov Model method and system

Disclosed is a system, method, and program storage device of aggregating opinions comprising consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions, generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of the discrete probability distributions, and presenting the aggregate opinion as a Bayesian network, wherein the divergences comprise Kullback-Liebler distance divergences, and wherein the expressed opinions are generated by experts and comprise opinions on sentiments of products and services. Moreover, the aggregate opinion predicts success of the products and services. Furthermore, the experts are arranged in a hierarchy of knowledge, wherein the knowledge comprises the various dimensions of topics for which opinions may be expressed upon.

Growing community of inventors

San Jose, CA, United States of America

Ashutosh Garg