Abstract: A system and method for generating frequency-multiplexed synthetic sound-speech stimuli and for detecting and analyzing electrical brain activity of a subject in response to the stimuli. Frequency-multiplexing of speech copora and synthetic sounds helps the composite sound to blend into a single auditory object. The synthetic sounds are temporally aligned with the utterances of the speech corpus. Frequency multiplexing may include splitting the frequency axis into alternating bands of speech and synthetic sound to minimize the disruptive interaction between the speech and synthetic sounds along the basilar membrane and in their neural representations. The generated stimuli can be used with both traditional and advanced techniques to analyze electrical brain activity and provides a rapid, synoptic view into the functional health of the early auditory system, including how speech is processed at different levels and how these levels interact.

Abstract: A Bayesian approach to training in conditional random fields takes a prior distribution over the modeling parameters of interest. These prior distributions may be used to generate an approximate form of a posterior distribution over the parameters, which may be trained with example or training data. Automatic relevance determination (ARD) may be integrated in the training to automatically select relevant features of the training data. From the trained posterior distribution of the parameters, a posterior distribution over the parameters based on the training data and the prior distributions over parameters may be approximated to form a training model. Using the developed training model, a given image may be evaluated by integrating over the posterior distribution over parameters to obtain a marginal probability distribution over the labels given that observational data.

Abstract: A discriminative machine learning system for labels text and non-text strokes in digital ink. The learning system considers stroke features and the context of the strokes, such as temporal information about one or more strokes, in a probabilistic framework. The learning system can also consider gap features within the probabilistic framework to label associated strokes.

Abstract: It is common in distributed systems to replicate data. In many cases, this data evolves in a consistent fashion, and this evolution can be modeled. A probabilistic model of the evolution allows us to estimate the divergence of the replicas and can be used by the application to alter its behavior, for example, to control synchronization times, to determine the propagation of writes, and to convey to the user information about how much the data may have evolved. In this paper, we describe how the evolution of the data may be modeled and outline how the probabilistic model may be utilized in various applications, concentrating on a news database example.

Abstract: A Bayesian treatment of mixture models is based on individual components having Student distributions, which have heavier tails compared to the exponentially decaying tails of Gaussians. The mixture of Student distribution components is characterized by a set of modeling parameters. Tractable approximations of the posterior distributions of individual modeling parameters are optimized and used to generate a data model for a set of input data.

Abstract: A variational Relevance Vector Machine (RVM) is disclosed. The RVM is a probabilistic basis model. Sparsity is achieved through a Bayesian treatment, where a prior is introduced over the weights governed by a set of what are referred to as hyperparameters—one such hyperparameter associated with each weight. An approximation to the joint posterior distribution over weights and hyperparameters is iteratively estimated from the data. The posterior distribution of many of the weights is sharply peaked around zero, in practice. The variational RVM utilizes a variational approach to solve the model, in particular using product approximations to obtain the posterior distribution.

Abstract: Bayesian principal component analysis. In one embodiment, a computer-implemented method for performing Bayesian PCA including inputting a data model; receiving a prior distribution of the data model; determining a posterior distribution; generating output data based on the posterior distribution (such as, a data model, a plurality of principal components, and/or a distribution); and, outputting the output data. In another embodiment, a computer-implemented method including inputting a mixture of a plurality of data spaces; determining a maximum number of principal components for each of the data spaces within the mixture; and, outputting the maximum number of principal components for each of the data spaces within the mixture.

Abstract: A variational inference engine for probabilistic graphical models is disclosed. In one embodiment, a method includes inputting a specification for a model that has observable variables and unobservable variables. The specification includes a functional form for the conditional distributions of the model, and a structure for a graph of model that has nodes for each of the variables. The method determines a distribution for the unobservable variables that approximates the exact posterior distribution, based on the graph's structure and the functional form for the model's conditional distributions. The engine thus allows a user to design, implement and solve models without mathematical analysis or computer coding.

Abstract: A fishing rod holder having a hip plate made of thin, flexible plastic, with a cylindrical tube, open at its top and bottom, attached to the hip plate. The tube slopes, from its bottom to its top, both forwardly and outwardly from the hip plate and supports a fishing rod, with the reel of the rod hanging and being supported on the top edge of the tube. When the hip plate is worn on a user's belt, the tip of the rod, and the hook, are located forwardly and to the side of the user, keeping them out of harm's way. The tube can be slidingly detachable from the hip plate and can be made in various forms to hold different kinds of fishing rods.