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Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets [article]

Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet
<span title="2019-06-10">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like Θ(n) in the number of data  ...  We propose the Scalable Metropolis-Hastings (SMH) kernel that exploits Gaussian concentration of the posterior to require processing on average only O(1) or even O(1/√(n)) data points per step.  ...  Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets: Supplementary Material A.  ... 
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Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet. "Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets." arXiv (2019)

Augur: a Modeling Language for Data-Parallel Probabilistic Inference [article]

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr
<span title="2014-06-10">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.  ...  It is time-consuming and error-prone to implement inference procedures for each new probabilistic model.  ...  Figaro focuses on a different set of inference techniques, including techniques which use exact inference in discrete spaces (they also have Metropolis-Hastings inference).  ... 
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Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr. "Augur: a Modeling Language for Data-Parallel Probabilistic Inference." arXiv (2014)

Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models [article]

Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz
<span title="2022-04-30">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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First, we introduce an accelerated feature proposal mechanism that we show is a valid MCMC algorithm for posterior inference.  ...  Inference of latent feature models in the Bayesian nonparametric setting is generally difficult, especially in high dimensional settings, because it usually requires proposing features from some prior  ...  A two-stage, asymptotically exact parallel inference procedure The method described in Section 3.2 is obviously not a correct MCMC sampler for a DPMM since it does not apply appropriate Metropolis-Hastings  ... 
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Michael Minyi Zhang, Sinead A. Williamson, Fernando Perez-Cruz. "Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models." arXiv (2022)

Bayesian Computation with Intractable Likelihoods [article]

Matthew T. Moores, Anthony N. Pettitt, Kerrie Mengersen
<span title="2020-04-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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to advancements in scalability for large datasets.  ...  This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood  ...  Acknowledgements This research was conducted by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (project number CE140100049) and funded by the Australian  ... 
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Matthew T. Moores, Anthony N. Pettitt, Kerrie Mengersen. "Bayesian Computation with Intractable Likelihoods." arXiv (2020)

Patterns of Scalable Bayesian Inference

Elaine Angelino, Matthew James Johnson, Ryan P. Adams
<span title="">2016</span> <i title="Now Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ka2h7lkphrfvjlabybgqbnn2jq" style="color: black;">Foundations and Trends® in Machine Learning</a> </i> &nbsp;

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There are a variety of methodological frameworks for statistical inference; here we are concerned with the Bayesian formalism.  ...  Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge.  ... 
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Citation

Elaine Angelino, Matthew James Johnson, Ryan P. Adams. "Patterns of Scalable Bayesian Inference." Foundations and Trends® in Machine Learning 9.2-3 (2016) 119-247

Scaling up Dynamic Topic Models

Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 25th International Conference on World Wide Web - WWW &#39;16</a> </i> &nbsp;

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We also present a Metropolis-Hastings based O(1) sampler for topic assignments for each word token.  ...  Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics.  ...  The dataset consists of news of 29 time slices, and we use 58 cores for our experiment, to infer a large Dynamic Topic Model.  ... 
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Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu. "Scaling up Dynamic Topic Models." Proceedings of the 25th International Conference on World Wide Web - WWW '16 (2016) 381-390

Scaling up Dynamic Topic Models [article]

Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu
<span title="2016-02-19">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We also present a Metropolis-Hastings based O(1) sampler for topic assignments for each word token.  ...  Due to a lack of a more scalable inference algorithm, despite the usefulness, DTMs have not captured large topic dynamics.  ...  For models with a large K, we often have K d ≪ K and Kw ≪ K.  ... 
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Citation

Arnab Bhadury, Jianfei Chen, Jun Zhu, Shixia Liu. "Scaling up Dynamic Topic Models." arXiv (2016)

Approximate Variational Inference for a Model of Social Interactions

Angelo Mele
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tol7woxlqjeg5bmzadeg6qrg3e" style="color: black;">Social Science Research Network</a> </i> &nbsp;

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This technique can be applied to any discrete exponential family, and therefore it is a general tool for inference in models with a large number of players.  ...  The methodology is illustrated with several simulated datasets and compared with MCMC methods.  ...  Variational Inference Methods Exact inference is impossible, because the evaluation of the likelihood is infeasible even for models with few players.  ... 
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Citation

Angelo Mele. "Approximate Variational Inference for a Model of Social Interactions." Social Science Research Network (2013)

Inferring signaling pathways with probabilistic programming

David Merrell, Anthony Gitter
<span title="2020-12-30">2020</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;

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A variety of techniques exist for inferring signaling pathways.  ...  Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.  ...  Acknowledgements We thank UW-Madison's Biomedical Computing Group for generously providing compute resources; the teams developing Snakemake and HTCondor (Thain et al., 2005) for empowering us to use  ... 
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<span class="ui label basic orange small" title="Open Access">Open Access</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210101082159/https://watermark.silverchair.com/btaa861.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAtUwggLRBgkqhkiG9w0BBwagggLCMIICvgIBADCCArcGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMdY8OkOpwkWGC7G0EAgEQgIICiCiBmvQY5YK0cb2N-Lz8v2ktd7ebUqqwhpd2QRRoKzG_Bu0iGFpFev0jMrsAf7tYdev-btzKdxX-9GicOKEqzJptOsooPmasUx3HHs_S14VLBpf8w3cCoeTBT79W0rxoko90lXNnW8fZJDYuFMWdy72wOhTKND1aMI4kEN3w0gnjyUOjxZfChwTFqxt49fWzvuwAHFqm1P-CJXai_RFM1ixO9axd5qLSRSlUto3nEecKusRbrg8RH38DdA-tQLlmjkf5cG4oYQtfQJhouuEOWstMftmtFq308_sSTjfRBuTT9qNoLteZi5xC7uN3kQcn-WVSEMDeHY5-7mYAuSKQe_-Bg4QGe8S9YH5jSeULIuHq4r7xmyI73vtvSm70Pk2qQzKnIN_1vEd16NbP9TAL952TMM6L16wWG0SWoPB9R7TZCe4PZmDiNrO4EisbR6jLIENEr53x5c4KXDEpRqAmU9fIvk656jh_6ic_NX6NEhZH1eQVZjrFjaHqG4F8Q-ED1imGMhdTFDbyK1mFOipUrEbjUPPYajn-Q8qitY447idBFFySs5Zpe_qumw5pT45ea73No_wP6jYJV7cKso1wsrVqFt8lhT4znPiNl_BKrIvqIjHusQZTBIU9g0W6_EJmABy5Te3GwOL87DJijxgYUJyF0hoe8LvrlmjbV-sQS4DZgVG6FHIq2Ni9p203o9Vwr8Enf-tJdBu_30GdjbUErLGdEBShDvwKZM9ItXrXzBGawPa21b4Ck08b0X_EM4wIrDpqk23F7S9tIYOmpGQdRDrs5dpAMKRbuMsvBUK80eVQWm9IfOdmlixqg3K9ipdLAM3GpYrLt7v5d2Yf4eZOFrhR1WKqVklPUw" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/da/c8da9ca96a5dc7f9d07aa5b3abc18a762d8119a0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btaa861"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> oup.com </button> </a>
Citation

David Merrell, Anthony Gitter. "Inferring signaling pathways with probabilistic programming." Bioinformatics 36.Supplement_2 (2020) i822-i830

Bayesian inference of ancestral dates on bacterial phylogenetic trees

Xavier Didelot, Nicholas J Croucher, Stephen D Bentley, Simon R Harris, Daniel J Wilson
<span title="2018-09-03">2018</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfp6p6inqbdexbsu4r7usndpte" style="color: black;">Nucleic Acids Research</a> </i> &nbsp;

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Here, we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics.  ...  , which makes our methodology much faster and scalable.  ...  its date • For each leaf of the tree with unknown sampling date, a Metropolis-Hastings move proposing to update its date • Two Metropolis-Hastings moves proposing to update the root location By default  ... 
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Xavier Didelot, Nicholas J Croucher, Stephen D Bentley, Simon R Harris, Daniel J Wilson. "Bayesian inference of ancestral dates on bacterial phylogenetic trees." Nucleic Acids Research 46.22 (2018)

Patterns of Scalable Bayesian Inference [article]

Elaine Angelino, Matthew James Johnson, Ryan P. Adams
<span title="2016-03-22">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge.  ...  As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference.  ...  E.A. is supported by the Miller Institute for Basic Research in Science, University of California, Berkeley. M.J. is supported by a fellowship from the Harvard/MIT Joint Grants program.  ... 
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Elaine Angelino, Matthew James Johnson, Ryan P. Adams. "Patterns of Scalable Bayesian Inference." arXiv (2016)

Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets [article]

Florian Maire, Nial Friel, Pierre Alquier
<span title="2018-05-31">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2017-06-26">2017</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1706.08327v1">arXiv:1706.08327v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iohu625l2vhideizhvlazreaoe">fatcat:iohu625l2vhideizhvlazreaoe</a> </span>
<span title="2017-12-11">2017</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1706.08327v2">arXiv:1706.08327v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2gta3annhndszc5oslichdf7hu">fatcat:2gta3annhndszc5oslichdf7hu</a> </span>
This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets.  ...  The resulting algorithm, Informed Sub-Sampling MCMC (ISS-MCMC), is a generic and flexible approach which, contrary to existing scalable methodologies, preserves the simplicity of the Metropolis-Hastings  ...  We thank the Associate Editor and two anonymous Referees for their contribution to this work. References  ... 
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Citation

Florian Maire, Nial Friel, Pierre Alquier. "Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets." arXiv (2018)

Bayesian inference of ancestral dates on bacterial phylogenetic trees [article]

Xavier Didelot, Nicholas J Croucher, Stephen D Bentley, Simon R Harris, Daniel J Wilson
<span title="2018-06-14">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Here we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics.  ...  , which makes our methodology much faster and scalable.  ...  of the tree with unknown sampling date, a Metropolis-Hastings move proposing to update its date • Two Metropolis-Hastings moves proposing to update the root location By default, the MCMC is run for a  ... 
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Citation

Xavier Didelot, Nicholas J Croucher, Stephen D Bentley, Simon R Harris, Daniel J Wilson. "Bayesian inference of ancestral dates on bacterial phylogenetic trees." bioRxiv (2018)

Scalable MCMC for Mixed Membership Stochastic Blockmodels [article]

Wenzhe Li, Sungjin Ahn, Max Welling
<span title="2015-10-22">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2015-10-16">2015</span> <span class="release-stage" >pre-print</span>
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We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB).  ...  In addition we develop an approximation that can handle models that entertain a very large number of communities.  ...  datasets ent computation and the Metropolis-Hastings acceptreject step.  ... 
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Wenzhe Li, Sungjin Ahn, Max Welling. "Scalable MCMC for Mixed Membership Stochastic Blockmodels." arXiv (2015)

A scalable, knowledge-based analysis framework for genetic association studies

James W Baurley, David V Conti
<span title="">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;

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We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak.  ...  We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma.  ...  Thomas for guidance in developing pathway-based methods. Christopher K. Edlund for ideas in developing the PEAK software.  ... 
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Citation

James W Baurley, David V Conti. "A scalable, knowledge-based analysis framework for genetic association studies." BMC Bioinformatics 14.1 (2013) 312

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