Scalable Bayes for Large and Streaming Sequential Data
Nick Foti, Emily B. Fox

Citation
Nick Foti, Emily B. Fox. "Scalable Bayes for Large and Streaming Sequential Data". Talk or presentation, October, 2015; Poster presented at the 2015 TerraSwarm Annual Meeting.

Abstract
Massive data streams arising from modern sensing modalities are becoming increasingly prevalent with the ubiquity of deploying these sensors, for example measuring energy consumption in smart buildings or the detection of air pollutants with a swarm of devices. Practitioners want to specify complex models of this data that capture the underlying patio-temporal dynamics in order to utilize the wealth of information contained within to effectively monitor, control, and make inferences about the underlying processes. However, existing inference algorithms for such data and models are either inappropriate for the case of an unknown amount of data arriving in a stream, or do not scale with the amount of data and the number of parameters to be practical. To address these issues we have been developing families of efficient algorithms to approximate the posterior distribution of Bayesian models for time-series data. In particular, we have extended stochastic variational inference (SVI), an approximate inference algorithm that operates on subsets of data, to handle the temporal dependencies in hidden Markov models (HMM). We have shown empirically that the algorithm can efficiently learn the parameters of an HMM with only a single pass through the data. Additionally, we have been developing truly streaming algorithm for Bayesian nonparametric (BNP) latent variable models for streaming data. BNP models allow the number of parameters in a model, e.g. the number of underlying clusters in a data set, to grow with the number of observations. Such models are ideally suited to the streaming setting where we do not know the amount of data we will see, but wish to be agnostic about the model complexity. We have derived an efficient streaming variational inference algorithm based on assumed density filtering for BNP mixture models to cluster large streams of observations.

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Citation formats

• HTML

  Nick Foti, Emily B. Fox. <a
  href="http://www.terraswarm.org/pubs/650.html"><i>Scalable
  Bayes for Large and Streaming Sequential
  Data</i></a>, Talk or presentation,  October,
  2015; Poster presented at the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• Plain text

  Nick Foti, Emily B. Fox. "Scalable Bayes for Large and
  Streaming Sequential Data". Talk or presentation, 
  October, 2015; Poster presented at the <a
  href="http://terraswarm.org/conferences/15/annual"
  >2015 TerraSwarm Annual Meeting</a>.

• BibTeX

  @presentation{FotiFox15_ScalableBayesForLargeStreamingSequentialData,
      author = {Nick Foti and Emily B. Fox},
      title = {Scalable Bayes for Large and Streaming Sequential
                Data},
      month = {October},
      year = {2015},
      note = {Poster presented at the <a
                href="http://terraswarm.org/conferences/15/annual"
                >2015 TerraSwarm Annual Meeting</a>.},
      abstract = {Massive data streams arising from modern sensing
                modalities are becoming increasingly prevalent
                with the ubiquity of deploying these sensors, for
                example measuring energy consumption in smart
                buildings or the detection of air pollutants with
                a swarm of devices. Practitioners want to specify
                complex models of this data that capture the
                underlying patio-temporal dynamics in order to
                utilize the wealth of information contained within
                to effectively monitor, control, and make
                inferences about the underlying processes.
                However, existing inference algorithms for such
                data and models are either inappropriate for the
                case of an unknown amount of data arriving in a
                stream, or do not scale with the amount of data
                and the number of parameters to be practical. To
                address these issues we have been developing
                families of efficient algorithms to approximate
                the posterior distribution of Bayesian models for
                time-series data. In particular, we have extended
                stochastic variational inference (SVI), an
                approximate inference algorithm that operates on
                subsets of data, to handle the temporal
                dependencies in hidden Markov models (HMM). We
                have shown empirically that the algorithm can
                efficiently learn the parameters of an HMM with
                only a single pass through the data. Additionally,
                we have been developing truly streaming algorithm
                for Bayesian nonparametric (BNP) latent variable
                models for streaming data. BNP models allow the
                number of parameters in a model, e.g. the number
                of underlying clusters in a data set, to grow with
                the number of observations. Such models are
                ideally suited to the streaming setting where we
                do not know the amount of data we will see, but
                wish to be agnostic about the model complexity. We
                have derived an efficient streaming variational
                inference algorithm based on assumed density
                filtering for BNP mixture models to cluster large
                streams of observations.},
      URL = {http://terraswarm.org/pubs/650.html}
  }
  

Posted by Emily B. Fox on 9 Oct 2015.
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