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A simple improved inferential method for some discrete distributions

Yong Cai, K. Krishnamoorthy
2005 Computational Statistics & Data Analysis  
In this article, some simple methods for testing and estimating the parameters of some discrete distributions are proposed.  ...  The methods are also illustrated for the hypergeometric and negative binomial distributions.  ...  In this article, we propose a simple approach to improve the results based on the classical exact method. For two-sided hypothesis testing, we propose a simple alternative exact test.  ... 
doi:10.1016/j.csda.2004.03.008 fatcat:75f6zyoyqfgk3oldekuzzrkwty

Differential analysis of RNA-Seq incorporating quantification uncertainty [article]

Harold J Pimentel, Nicolas Bray, Suzette Puente, Páll Melsted, Lior Pachter
2016 bioRxiv   pre-print
We describe a novel method for the differential analysis of RNA-Seq data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential  ...  The method is implemented in an interactive shiny app called sleuth that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of RNA-Seq experiments.  ...  We thank Daniel Li, Alex Tseng, Pascal Sturmfels for help with implementing some of the interactive features in sleuth.  ... 
doi:10.1101/058164 fatcat:6vvbywg7jzfrxdciqkbik4wdxy

Differential analysis of RNA-seq incorporating quantification uncertainty

Harold Pimentel, Nicolas L Bray, Suzette Puente, Páll Melsted, Lior Pachter
2017 Nature Methods  
We describe a novel method for the differential analysis of RNA-Seq data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential  ...  The method is implemented in an interactive shiny app called sleuth that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of RNA-Seq experiments.  ...  We thank Daniel Li, Alex Tseng, Pascal Sturmfels for help with implementing some of the interactive features in sleuth.  ... 
doi:10.1038/nmeth.4324 pmid:28581496 fatcat:mlonthd76ja4zfi6zhjrn4qaji

A User's Guide to Principal Components

Jim Freeman
1992 Journal of the Operational Research Society  
Vector Interpretation II: Rotation 8.1 Introduction, 155 8.2 Simple Structure, 156 8.3 Simple Rotation, 157 8.4 Rotation Methods, 159 8.5 Some Comments About Rotation, 165 8.6 Procrustes Rotation  ...  Fiatland: Special Procedures for Two Dimensions 342 15.1 Construction of a Probability Ellipse, 342 15.2 Inferential Procedures for the Orthogonal Regression Line, 344 15.3 Correlation Matrices,  ... 
doi:10.1057/jors.1992.90 fatcat:mtzif565zvdhtduq3idsxmgx5m

Introductory Life Science Mathematics and Quantitative Neuroscience Courses

Dwight Duffus, Andrei Olifer, John Jungck
2010 CBE - Life Sciences Education  
We provide a description of each course, detailed syllabi, examples of content, and a brief discussion of the main issues encountered in developing and offering the courses.  ...  The first course is an introductory sequence in differential and integral calculus, modeling with differential equations, probability, and inferential statistics.  ...  Statistical methods included descriptive and inferential statistics, with emphasis on hypothesis testing, sampling distributions, and analysis of variance.  ... 
doi:10.1187/cbe.10-03-0026 pmid:20810971 pmcid:PMC2931686 fatcat:bime5ex3bngipmigwdyv6upc6y

Recognition of Human Actions using Layered Hidden Markov Models [article]

Serafeim Perdikis, Dimitrios Tzovaras, Michael Gerasimos Strintzis
2008 Eurographics State of the Art Reports  
This paper proposes a method which employs a hierarchical structure of Hidden Markov Models (Layered HMMs) in an attempt to exploit inherent characteristics of human action for more efficient recognition  ...  The results demonstrate the efficiency, the tolerance on noise interpolation and the high degree of person - invariance of the method.  ...  Thus, successive outputs of some layer form the (discrete) input vectors of the next one.  ... 
doi:10.2312/egs.20081026 fatcat:zcegvxw6izf6bbp33npxpajln4

Page 6133 of Mathematical Reviews Vol. , Issue 91K [page]

1991 Mathematical Reviews  
C. (3-CALG) Geeta distribution and its properties. Comm. Statist. Theory Methods 19 (1990), no. 8, 3051-3068. Summary: “A new discrete distribution defined over all the positive  ...  X3/2, For this model, Fop(t) based on the (incorrect) simple lin- ear regression model is unacceptably biased for some values of t, but Frxm(t), also based on the same (incorrect) model, performs 62E Distribution  ... 

Improving VAE generations of multimodal data through data-dependent conditional priors [article]

Frantzeska Lavda, Magda Gregorová, Alexandros Kalousis
2019 arXiv   pre-print
This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the data generations.  ...  We assume a two-level generative process with a continuous (Gaussian) latent variable sampled conditionally on a discrete (categorical) latent component.  ...  This simple set-up allows us to better understand the strengths and weaknesses of the method in terms of its density estimation performance for a known and rather simple ground-truth data distribution.  ... 
arXiv:1911.10885v1 fatcat:detu52wo6zfjvp7mzykaznexba

Bayesian methods in bioinformatics and computational systems biology

D. J. Wilkinson
2006 Briefings in Bioinformatics  
Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data.  ...  Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that is uncertain or subject to any kind of error or noise (including measurement error and experimental  ...  A basic inferential algorithm for this model is described in [92] .  ... 
doi:10.1093/bib/bbm007 pmid:17430978 fatcat:x4veqsjajbgnrm6x27ecv3glqm

Philosophically Inspired Concept Acquisition for Artificial General Intelligence [chapter]

Iris Oved, Ian Fasel
2011 Lecture Notes in Computer Science  
The hypothesized kind/category is given a name and inferential relationships are stored between the name and representations for the observable properties.  ...  We argue that this process appeases tensions in the philosophical debate by allowing for the acquisition of concepts via perception and inference, while yielding the concepts simple, in the sense of being  ...  Many methods are possible for learning such a model.  ... 
doi:10.1007/978-3-642-22887-2_44 fatcat:a3kyvg2a4nasdceqxblmrx4wyi

Bringing Order to the Chaos in the Brickyard [article]

Bethany Shifflett
2016 arXiv   pre-print
This paper is intended to clarify some of the challenges, particularly with respect to quantitative research, then suggest ways to improve the quality of published research.  ...  The paper highlights where feasible refinements in analytical techniques can be made and provides a guide to fundamental principles related to data analysis in research.  ...  the dependent variable are discrete, ).  ... 
arXiv:1606.03189v1 fatcat:pocle2bup5ailezxayiytud3mi

How Much Information Per Joule? Measuring the Energy Efficiency of Inferential Wireless Sensing

Paul G. Flikkema
2011 2011 IEEE International Conference on Communications Workshops (ICC)  
The inferential energy efficiency, based on the RDD, is a measure of the "fuel economy" of inferential sensing.  ...  It is nonasymptotic, and, as part of a Bayesian inference framework, does not require a prior distribution on the data model but can accommodate prior information on process model parameters.  ...  Clark and A. Gelfand for informative and stimulating discussions. This work was supported by NSF grant CNS-0540414.  ... 
doi:10.1109/iccw.2011.5963538 fatcat:jtopfxeiqfeltlfmmly4lxdf74

Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures

David Darmon
2020 Entropy  
We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic measures via confidence distributions using estimates  ...  However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis  ...  Input: A time series x T 1 from a discrete-state, discrete-time stochastic process. Output: A confidence distribution C(θ) for a measure θ. 1.  ... 
doi:10.3390/e22070782 pmid:33286553 fatcat:3h62sdqslzcqxc3gu5xqa5hhzm

Page 3250 of Psychological Abstracts Vol. 87, Issue 9 [page]

2000 Psychological Abstracts  
meta-analysis, examples of data-driven/computer-intensive methods, generation and analysis of pseudorandom numbers, discrete probability distribution, outliers, and other topics Statistics & Mathematics  ...  The chi-square test for r x c tables [Test 16a: The chi-square test for homogeneity; Test 16b: The chi-square test of independence (employed with a single sample)| * Inferential statistical tests employed  ... 

Nonparametric expression analysis using inferential replicate counts

2019 Nucleic Acids Research  
We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty.  ...  Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty.  ...  ACKNOWLEDGEMENTS We thank Naim Rashid, Scott Van Buren, Yun Li, Jason Stein, Jeremy Simon and Frank Konietschke for useful discussions.  ... 
doi:10.1093/nar/gkz622 pmid:31372651 pmcid:PMC6765120 fatcat:3wmstsdauzgv5gsf45ebb3b3ee
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