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Results 1 - 10
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333
Universal models for binary spike patterns using centered Dirichlet processes
"... Probabilistic models for binary spike patterns provide a powerful tool for un-derstanding the statistical dependencies in large-scale neural recordings. Maxi-mum entropy (or “maxent”) models, which seek to explain dependencies in terms of low-order interactions between neurons, have enjoyed remarkab ..."
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Cited by 1 (1 self)
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of “universal ” models for binary spike pat-terns, where universality refers to the ability to model arbitrary distributions over all 2m binary patterns. We construct universal models using a Dirichlet process centered on a well-behaved parametric base measure, which naturally combines the flexibility of a
Spike train entropy-rate estimation using hierarchical dirichlet process priors
- Advances in Neural Information Processing Systems
, 2013
"... Entropy rate quantifies the amount of disorder in a stochastic process. For spiking neurons, the entropy rate places an upper bound on the rate at which the spike train can convey stimulus information, and a large literature has focused on the prob-lem of estimating entropy rate from spike train dat ..."
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data. Here we present Bayes least squares and empirical Bayesian entropy rate estimators for binary spike trains us-ing hierarchical Dirichlet process (HDP) priors. Our estimator leverages the fact that the entropy rate of an ergodic Markov Chain with known transition prob-abilities can be calculated
A Spike and Slab centering Distribution in Dirichlet Process Mixture Models for Gene expression
"... Model-based clustering methods using Dirichlet process (DP) mixture models have been pro-posed to exploit clustering for increased sensitivity in multiple hypothesis testing. Rather than yielding a probability of a hypothesis for each object, existing methods can only provide a rank-ing of the objec ..."
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Model-based clustering methods using Dirichlet process (DP) mixture models have been pro-posed to exploit clustering for increased sensitivity in multiple hypothesis testing. Rather than yielding a probability of a hypothesis for each object, existing methods can only provide a rank-ing
doi:10.1155/2010/201489 Research Article Spiked Dirichlet Process Priors for Gaussian Process Models
"... the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We expand a framework for Bayesian variable selection for Gaussian process �GP � models by employing spiked Dirichlet process �DP � pr ..."
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the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We expand a framework for Bayesian variable selection for Gaussian process �GP � models by employing spiked Dirichlet process �DP
Spiked dirichlet process prior for bayesian multiple hypothesis testing in random effects models
- Bayesian Analysis
, 2009
"... Abstract. We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditi ..."
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Abstract. We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment
Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
, 1992
"... We describe an immune system model based on a universe of binary strings. The model is directed at understanding the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of our mod ..."
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Cited by 82 (6 self)
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We describe an immune system model based on a universe of binary strings. The model is directed at understanding the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of our
Whom You Know Matters: Venture Capital Networks and Investment Performance,
- Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
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Cited by 138 (8 self)
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the results reported in the following sections utilize the binary matrix, we note that all our results are robust to using network centrality measures calculated from valued matrices. 6 Unlike the undirected matrix, the directed matrix does not record a tie between VCs j and k who were members of the same
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
- In Advances in Neural Information Processing Systems (NIPS
, 2013
"... Shannon’s entropy is a basic quantity in information theory, and a fundamental building block for the analysis of neural codes. Estimating the entropy of a dis-crete distribution from samples is an important and difficult problem that has re-ceived considerable attention in statistics and theoretica ..."
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Cited by 2 (2 self)
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us-ing mixtures of Dirichlet distributions centered on simple parametric models. The parametric model captures high-level statistical features of the data, such as the average spike count in a spike word, which allows the posterior over entropy to concentrate more rapidly than with standard
Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings
"... Recent studies compare gene expression data across species to identify core and species specific genes in biological systems. To perform such comparisons researchers need to match genes across species. This is a challenging task since the correct matches (orthologs) are not known for most genes. Pre ..."
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Cited by 2 (0 self)
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in both species. Our method uses a Dirichlet process mixture model which includes a latent data matching variable. We present learning and inference algorithms based on variational methods for this model. Applying our method to immune response data we show that it can accurately identify common and unique
likelihood and the role of models in molecular phylogenetics.
- Mol. Biol. Evol.
, 2000
"... Methods such as maximum parsimony (MP) are frequently criticized as being statistically unsound and not being based on any ''model.'' On the other hand, advocates of MP claim that maximum likelihood (ML) has some fundamental problems. Here, we explore the connection between the ..."
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Cited by 70 (11 self)
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;'conserved'' and ''hypervariable'' sites (so the real process is definitely not i.i.d. across sites), but when one passes to the frequencies of site patterns (i.e., the data D), the process can be modeled by an i.i.d. process. Similarly, certain covarion-style mechanisms (where sites can alternate
Results 1 - 10
of
333
