Parameterized distributions

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Lecture Artificial Intelligence - Chapter 14a: Bayesian networks

Lecture Artificial Intelligence - Chapter 14a: Bayesian networks. The main contents of this chapter include all of the following: Bayes nets provide a natural representation for (causally induced) conditional independence, topology + CPTs = compact representation of joint distribution, Generally easy for (non)experts to construct, Canonical distributions (e.g., noisy-OR) = compact representation of CPTs, Continuous variables ⇒ parameterized distributions (e.g., linear Gaussian).

29p cucngoainhan0 10-05-2022 11 2 Download

MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features.

13p viaristotle 29-01-2022 13 0 Download

On relation between one multiple and a corresponding one-dimensional integral with applications

For a given finite positive measure on an interval I ⊆ R, a multiple stochastic integral of a Volterra kernel with respect to a product of a corresponding Gaussian orthogonal stochastic measure is introduced. The Volterra kernel is taken such that the multiple stochastic integral is a multiple iterated stochastic integral related to a parameterized Hermite polynomial, where parameter depends on Gaussian distribution of an underlying one-dimensional stochastic integral.

14p danhnguyentuongvi27 19-12-2018 11 0 Download
Báo cáo khoa học: "PCFGs, Topic Models, Adaptor Grammars and Learning Topical Collocations and the Structure of Proper Names"

This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as “topic models” to produce a lowdimensional representation of documents, while Probabilistic Context-Free Grammars (PCFGs) deﬁne distributions over trees. The paper begins by showing that LDA topic models can be viewed as a special kind of PCFG, so Bayesian inference for PCFGs can be used to infer Topic Models as well. Adaptor Grammars (AGs) are a hierarchical, non-parameteric Bayesian extension of PCFGs. ...

10p hongdo_1 12-04-2013 58 2 Download