scholarly journals On Parameter Estimation Approaches for Predicting Disease Transmission Through Optimization, Deep Learning and Statistical Inference Methods

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Mazair Raissi ◽  
Niloofar Ramezani ◽  
Padmanabhan Seshaiyer
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Statistical Inference ◽  
Disease Transmission ◽  
Inference Methods

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.

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 Statistically-informed deep learning for gravitational wave parameter estimation

2021 ◽  
Author(s):  
Hongyu Shen ◽  
Eliu Huerta ◽  
Eamonn O’Shea ◽  
Prayush Kumar ◽  
Zhizhen Zhao
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Network Models ◽  
Wave Parameter ◽  
Posterior Distributions ◽  
Neural Network Models ◽  
The Masses ◽  
Bayesian Methodologies

Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, (m1, m2), and three astrophysical properties of the post-merger compact remnant, namely, the final spin, af, and the frequency and damping time of the ringdown oscillations of the fundamental (l=m=2) bar mode, (ωR, ωI). Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters (m1, m2, af, ωR, ωI) of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90\% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

Statistical inference for point-process models of rainfall

1984 ◽  
Vol 16 (1) ◽  
pp. 22-22 ◽  
Author(s):  
James A. Smtih ◽  
Alan F. Karr
Keyword(s):  
Parameter Estimation ◽  
Statistical Inference ◽  
Point Processes ◽  
Process Models ◽  
Renewal Processes ◽  
Cox Processes ◽  
The Family ◽  
Point Process Models ◽  
Image Position ◽  
Rainfall Occurrences

In this paper we develop maximum likelihood procedures for parameter estimation and hypothesis testing for three classes of point processes that have been used to model rainfall occurrences; renewal processes, Neyman-Scott processes, and RCM processes (which are members of the family of Cox processes). The statistical inference procedures developed in this paper are based on the intensity process

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