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Bayesian Neural Networks with Dependent Dirichlet Process Priors. Application to Pairs Trading

RUXANDA GHEORGHE, OPINCARIU SORIN
<span title="2018-12-18">2018</span> <i title="Bucharest University of Economic Studies"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5qkv74cv6vfnzinalo363yroaq" style="color: black;">ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH</a> </i> &nbsp;
In this paper we propose a hierarchical model where the prior distribution of the network weights is drawn from a Dirichlet process mixture model.  ...  We further extend the model to dependent Dirichlet process mixtures to allow the model to account for non-stationarity in the data.  ...  The base measure of the Dirichlet process mixture is a product of a gamma and a uniform distribution, here we follow the guidelines of Gelman [20] for choosing the priors for variance parameters.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24818/18423264/52.4.18.01">doi:10.24818/18423264/52.4.18.01</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qdjjp7ttsncprcq4472kmwraay">fatcat:qdjjp7ttsncprcq4472kmwraay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220304085026/http://www.ecocyb.ase.ro/nr2018_4/01%20-%20Ruxanda%20Gh.,%20Sorin%20Opincariu.pdf" 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/b3/26/b32600cb0b3e0f77aaf7c38553042e78f3db502a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24818/18423264/52.4.18.01"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Nonparametric Hierarchical Bayesian Models for Positive Data Clustering Based on Inverted Dirichlet-Based Distributions

Wentao Fan, Nizar bouguila
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Clustering, mixture models, inverted Dirichlet, nonparametric Bayesian model, stochastic variational inference.  ...  The inference for the resulting models takes into account the challenging problem of feature weighting/selection and is conducted under a Bayesian setting by means of the recently proposed stochastic variational  ...  APPENDIX A STOCHASTIC VARIATIONAL INFERENCE OF THE HPY PROCESS MIXTURE MODEL WITH ID DISTRIBUTIONS A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2924651">doi:10.1109/access.2019.2924651</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6at4tmwtlzeuxfrbhfher7uyui">fatcat:6at4tmwtlzeuxfrbhfher7uyui</a> </span>
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Region Segmentation Based On Gaussian Dirichlet Process Mixture Model And Its Application To 3D Geometric Stricture Detection

Jonghyun Park, Soonyoung Park, Sanggyun Kim, Wanhyun Cho, Sunworl Kim
<span title="2012-04-21">2012</span> <i title="Zenodo"> Zenodo </i> &nbsp;
In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model.  ...  In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting and Dirichlet process mixture model to a image segmentation.  ...  Gaussian Dirichlet Process Mixture Model One of the most important applications of the Dirichlet processes is as a nonparametric prior distribution of a mixture model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1333021">doi:10.5281/zenodo.1333021</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wtporw74yjhstdiymxpt7czsyu">fatcat:wtporw74yjhstdiymxpt7czsyu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200221082332/https://zenodo.org/record/1333022/files/7008.pdf" 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/7c/e7/7ce7fd353fb411458d7ec82dcab6ec4ed392a50e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.1333021"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Online Data Clustering Using Variational Learning of a Hierarchical Dirichlet Process Mixture of Dirichlet Distributions [chapter]

Wentao Fan, Nizar Bouguila
<span title="">2014</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The resulting statistical model is learned using variational Bayes and is evaluated via a challenging application namely images clustering.  ...  This paper proposes an online clustering approach based on both hierarchical Dirichlet processes and Dirichlet distributions.  ...  The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-662-43984-5_2">doi:10.1007/978-3-662-43984-5_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/axd7wvrofbe6zc4kgl6agouzha">fatcat:axd7wvrofbe6zc4kgl6agouzha</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814185449/http://www.springer.com/cda/content/document/cda_downloaddocument/9783662439838-c2.pdf?SGWID=0-0-45-1467023-p176818856" 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/42/c6/42c6d66faffe7d78162cbd70403bf0a5c4a6fea5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-662-43984-5_2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

Norah Saleh Alghamdi, Sami Bourouis, Nizar Bouguila
<span title="">2022</span> <i title="Computers, Materials and Continua (Tech Science Press)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/amujz7fcqna6do727z6ev3ueo4" style="color: black;">Computers Materials &amp; Continua</a> </i> &nbsp;
The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions.  ...  In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed.  ...  In the case of hierarchical Dirichlet process (HDP), the DPs for all groups share a base distribution which is itself distributed according to a Dirichlet process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.022959">doi:10.32604/cmc.2022.022959</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dzz54vf6e5ekzn6pbngst65awe">fatcat:dzz54vf6e5ekzn6pbngst65awe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220427155145/https://www.techscience.com/ueditor/files/cmc/TSP_CMC-72-1/TSP_CMC_22959/TSP_CMC_22959.pdf" 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/3a/d1/3ad1ff3f1a2504be701accd1bc1ec8af392ac2ae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.022959"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

An Introduction to Bayesian Inference via Variational Approximations

Justin Grimmer
<span title="">2011</span> <i title="Cambridge University Press (CUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7ysuj5jirzc5dkdoksxiwc72py" style="color: black;">Political Analysis</a> </i> &nbsp;
In this paper, we show how a recently developed tool in computer science for fitting Bayesian models, variational approximations, can be used to facilitate the application of Bayesian models to political  ...  Variational approximations are often much faster than MCMC for fully Bayesian inference and in some instances facilitate the estimation of models that would be otherwise impossible to estimate.  ...  This demonstrates the strengths and potential weaknesses of using a variational approximation for Bayesian inference.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/pan/mpq027">doi:10.1093/pan/mpq027</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7diaacxf7zcclpeezik23mgne4">fatcat:7diaacxf7zcclpeezik23mgne4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430210130/https://www.cambridge.org/core/services/aop-cambridge-core/content/view/ACFDDB331CD4AFBD39557ABDB8A3C4E6/S1047198700012638a.pdf/div-class-title-an-introduction-to-bayesian-inference-via-variational-approximations-div.pdf" 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/f9/3b/f93b81865ab2656f19dd8fdc771052cb55e570b5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/pan/mpq027"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A tutorial on Bayesian nonparametric models

Samuel J. Gershman, David M. Blei
<span title="">2012</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/euneo6ybs5daxjpykphojxg2tu" style="color: black;">Journal of Mathematical Psychology</a> </i> &nbsp;
This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.  ...  In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model.  ...  The Bayesian nonparametric mixture model, which is called a Chinese restaurant process mixture (or a Dirichlet process mixture), infers the number of clusters from the data and allows the number of clusters  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jmp.2011.08.004">doi:10.1016/j.jmp.2011.08.004</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/allxc5i5qbcnvazdss67z7qw3y">fatcat:allxc5i5qbcnvazdss67z7qw3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160614230624/http://www.cs.columbia.edu:80/~blei/papers/GershmanBlei2012.pdf" 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/df/82/df827f04a40f4396971ecc7028ae2750904cfced.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jmp.2011.08.004"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Nonparametric Bayesian methods: a gentle introduction and overview

Steven N. MacEachern
<span title="2016-11-30">2016</span> <i title="The Korean Statistical Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fzo5sijrfbc2loalfsuppgvz5a" style="color: black;">Communications for Statistical Applications and Methods</a> </i> &nbsp;
We then step through the various constructions of the Dirichlet process, outline a number of the basic properties of this process and move on to the mixture of Dirichlet processes model, including a quick  ...  We touch on the main philosophies for nonparametric Bayesian data analysis and then reanalyze a famous data set.  ...  This paper This paper closely follows a tutorial presented at the meeting of the Bayesian Statistics Section of the Korean Statistical Society on Jeju Island in 2016.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5351/csam.2016.23.6.445">doi:10.5351/csam.2016.23.6.445</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p5mu2jz3pfawzbraklq23zqqji">fatcat:p5mu2jz3pfawzbraklq23zqqji</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200213174336/http://www.csam.or.kr/journal/download_pdf.php?doi=10.5351/CSAM.2016.23.6.445" 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/c6/60/c6601e80f28908de6f42eba8abd3b0f03d10ce9d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5351/csam.2016.23.6.445"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Infinite Mixture of Inverted Dirichlet Distributions [article]

Zhanyu Ma, Yuping Lai
<span title="2020-02-02">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we develop a novel Bayesian estimation method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors  ...  Moreover, the problems of over-fitting and under-fitting are avoided by the Bayesian estimation approach.  ...  Dirichlet Process with Stick-Breaking The Dirichlet process (DP) [31] , [32] is a stochastic process used for Bayesian nonparametric data analysis, particularly in a DP mixture model (infinite mixture  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.10693v2">arXiv:1807.10693v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m4ay2o4kwjcghm53npaad5vsoa">fatcat:m4ay2o4kwjcghm53npaad5vsoa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321161241/https://arxiv.org/pdf/1807.10693v2.pdf" 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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.10693v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Variational nonparametric Bayesian Hidden Markov Model

Nan Ding, Zhijian Ou
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2010 IEEE International Conference on Acoustics, Speech and Signal Processing</a> </i> &nbsp;
Based on the Dirichlet Process, a nonparametric Bayesian Hidden Markov Model is proposed, which allows an infinite number of hidden states and uses an infinite number of Gaussian components to support  ...  Our experiments demonstrate that the variational Bayesian inference on the new model can discover the HMM hidden structure for both synthetic data and real-world applications.  ...  Some of the nonparametric Bayesian models such as the Dirichlet Process [3, 4] and the Indian Buffet Process [5] have been widely applied.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2010.5495125">doi:10.1109/icassp.2010.5495125</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/DingO10.html">dblp:conf/icassp/DingO10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mdudclf7jzfynojtvraeherjju">fatcat:mdudclf7jzfynojtvraeherjju</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810174140/http://oa.ee.tsinghua.edu.cn/~ouzhijian/pdf/ic10_nbhmm.pdf" 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/9e/e9/9ee9330f630e3872cd26915b9a2c6439c21850b1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2010.5495125"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Computational challenges and temporal dependence in Bayesian nonparametric models

Raffaele Argiento, Matteo Ruggiero
<span title="2017-09-09">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hch3zkatnfg3bledcpvafteg74" style="color: black;">Statistical Methods &amp; Applications</a> </i> &nbsp;
provide an excellent review of several classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison  ...  Here we contribute by concisely discussing general computational challenges which arise with posterior inference with Bayesian nonparametric models and certain aspects of modelling temporal dependence.  ...  For example, if p := G(A) ∈ [0, 1] for some fixed set A, the law of G(A) is a beta distribution and L(dθ i | G(A)) is Bernoulli with parameter p, then the above reduces to a beta-binomial transition P  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10260-017-0397-8">doi:10.1007/s10260-017-0397-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/myder2uvz5b5jisbdkk55kxlcm">fatcat:myder2uvz5b5jisbdkk55kxlcm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719174235/https://iris.unito.it/retrieve/handle/2318/1652936/372531/final.pdf" 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/72/38/7238acbb5f5661844fa4522a7f6b1e2eead40296.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10260-017-0397-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

An Infinite Multivariate Categorical Mixture Model for Self-Diagnosis of Telecommunication Networks

Amine Echraibi, Joachim Flocon-Cholet, Stephane Gosselin, Sandrine Vaton
<span title="">2020</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/m6zsy4ocifbjrgaulhy4hcbpha" style="color: black;">2020 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)</a> </i> &nbsp;
The model is based on the Dirichlet Process [4], which allows for learning the number of clusters from the data.  ...  Furthermore, classical approaches such as KMeans suppose a specific probability distribution for each cluster.  ...  on the Dirichlet Process Categorical Mixture Model using variational inference.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icin48450.2020.9059491">doi:10.1109/icin48450.2020.9059491</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icin/EchraibiFGV20.html">dblp:conf/icin/EchraibiFGV20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qtuid6a4zjgupjgubpykrqdohu">fatcat:qtuid6a4zjgupjgubpykrqdohu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200510011637/https://hal.archives-ouvertes.fr/hal-02431732/file/ICIN_paper_2020%284%29%20%281%29.pdf" 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/32/a6/32a610c7a91a9311da803b16bea601a84534ea1e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icin48450.2020.9059491"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A Tutorial on Bayesian Nonparametric Models [article]

Samuel J. Gershman, David M. Blei
<span title="2011-08-04">2011</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.  ...  In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model.  ...  Sloan foundation, and a grant from Google.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1106.2697v2">arXiv:1106.2697v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2s3yprihfzc4rnnhgo3yfpfxxq">fatcat:2s3yprihfzc4rnnhgo3yfpfxxq</a> </span>
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Variational inference for Dirichlet process mixtures

David M. Blei, Michael I. Jordan
<span title="">2006</span> <i title="Institute of Mathematical Statistics"> Bayesian Analysis </i> &nbsp;
Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled their  ...  In this paper, we present a variational inference algorithm for DP mixtures.  ...  We have developed a mean-field variational inference algorithm for the Dirichlet process mixture model and demonstrated its applicability to the kinds of multivariate data for which Gibbs sampling algorithms  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1214/06-ba104">doi:10.1214/06-ba104</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u3utwh7t3bdttl6nksk4g3km5m">fatcat:u3utwh7t3bdttl6nksk4g3km5m</a> </span>
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Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics

Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
<span title="2015-02-01">2015</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3px634ph3vhrtmtuip6xznraqi" style="color: black;">IEEE Transactions on Pattern Analysis and Machine Intelligence</a> </i> &nbsp;
For the Dirichlet process mixture models used in density estimation and clustering, the parameter space is dense in the space of probability measures.  ...  For Gaussian process models of regression and classification functions, the parameter space consists of a set of continuous functions.  ...  a target Dirichlet process mixture model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tpami.2014.2380478">doi:10.1109/tpami.2014.2380478</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26598765">pmid:26598765</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cuulndonrff4ffvneirnnzariy">fatcat:cuulndonrff4ffvneirnnzariy</a> </span>
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