Pavel N. Krivitsky | DeepAI

DeepAI AI Chat

Featured Co-authors

Rohitash Chandra
33 publications
David R. Hunter
6 publications
Niel Hens
6 publications
Martina Morris
3 publications
Chad Klumb
3 publications
Manavendra Maharana
2 publications
Pietro Coletti
2 publications
Mahir Jain
1 publication
Alina R. Kuvelkar
1 publication
Ayush Bhagat
1 publication

∙ 03/15/2022

ergm 4: Computational Improvements

The ergm package supports the statistical analysis and simulation of net...

0 Pavel N. Krivitsky, et al. ∙

∙ 03/14/2022

Modeling Tie Duration in ERGM-Based Dynamic Network Models

Krivitsky and Handcock (2014) proposed a Separable Temporal ERGM (STERGM...

0 Pavel N. Krivitsky, et al. ∙

∙ 03/14/2022

Modeling of Dynamic Networks based on Egocentric Data with Durational Information

Modeling of dynamic networks – networks that evolve over time – has mani...

0 Pavel N. Krivitsky, et al. ∙

∙ 02/08/2022

A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks

The last two decades have witnessed considerable progress on foundationa...

0 Pavel N. Krivitsky, et al. ∙

∙ 02/08/2022

Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming

This article discusses the problem of determining whether a given point,...

0 Pavel N. Krivitsky, et al. ∙

∙ 06/09/2021

ergm 4.0: New features and improvements

The ergm package supports the statistical analysis and simulation of net...

0 Pavel N. Krivitsky, et al. ∙

∙ 04/17/2021

Bayesian graph convolutional neural networks via tempered MCMC

Deep learning models, such as convolutional neural networks, have long b...

0 Rohitash Chandra, et al. ∙

∙ 04/13/2021

Revisiting Bayesian Autoencoders with MCMC

Autoencoders gained popularity in the deep learning revolution given the...

0 Rohitash Chandra, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world