Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions.

1994 ◽  
Vol 89 (427) ◽  
pp. 1142
Author(s):  
Bradley P. Carlin ◽  
Martin A. Tanner
Keyword(s):  
Statistical Inference ◽  
Posterior Distributions ◽  
Likelihood Functions ◽  
Inference Methods

scholarly journals Buchbesprechungen

2016 ◽  
Vol 25 (2) ◽  
Author(s):  
Josef Roppert ◽  
Jürgen Pilz ◽  
Sylvia Frühwirth-Schnatter ◽  
Walter Katzenbeisser ◽  
Reinhard Viertl ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Wavelet Analysis ◽  
Statistical Inference ◽  
Nonlinear Regression ◽  
Observational Studies ◽  
Statistical Modelling ◽  
Statistical Tools ◽  
Likelihood Functions ◽  
Inference Methods

Probability (A.N. Shiryaev)Advanced Statistics. Volume I: Description of Populations (S.J. Haberman)Tools for Statistical Inference. Methods for the Exploration of Posterior Distributi-ons and Likelihood Functions (M.A Tanner)Plane Answers to Complex Questions. The Theory of LinearModels (R. Christensen)Statistical Tools for Nonlinear Regression (S. Huet, A. Bouvier, M. Gruet und E. Jolivet)Handbook of Brownian Motion: Facts and Formulae (A.N. Borodin und P. Salminen)Bayes’sche Statistik für kontrollierte Experimente (K. Felsenstein)Applied Wavelet Analysis with S-Plus (A. Bruce und H. Gao)Observational Studies (P.R. Rosenbaum)Activity-Based Statistics (R.L. Scheaffer, M. Gnanadesikan, A.Watkins und J.Witmer)Statistical Modelling (G.U.H. Seeber, B.J. Francis, R. Hatzinger und G. Steckel-Berger)

scholarly journals Interpretable factor models of single-cell RNA-seq via variational autoencoders

2019 ◽  
Author(s):  
Valentine Svensson ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Statistical Inference ◽  
Factor Models ◽  
Rna Seq ◽  
Cell Type ◽  
Massive Datasets ◽  
Domain Specific ◽  
Variational Autoencoder ◽  
Inference Methods

Single cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be of interest for other applications.

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