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Snijders Scientific mcmc algorithms
Mcmc Algorithms, supplied by Snijders Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mcmc+algorithms/mcmc+algorithms/pmc11741508-61-6-15
Average 90 stars, based on 1 article reviews
mcmc algorithms - by Bioz Stars, 2026-09
90/100 stars

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Article Title: Dynamics of Population Flow Networks
Article Snippet: While it is known that conventional MCMC algorithms can in principle mix arbitrarily slowly (Snijders, 2002; Bhamidi et al., 2008), in actual practice this problem has been observed primarily in badly specified models that are degenerate or near-degenerate, and hence of limited relevance in typical social network applications (Hunter et al., 2012).

Article Title: Parameter Estimation Procedures for Exponential-family Random Graph Models on Count-valued Networks: A Comparative Simulation Study
Article Snippet: While it is known that conventional MCMC algorithms can in principle mix arbitrarily slowly ( Snijders, 2002 ; Bhamidi et al., 2011 ), in actual practice this problem has been observed primarily in badly specified models that are degenerate or near-degenerate, and hence of limited relevance in typical social network applications ( Hunter et al., 2012 ).

Article Title: New Specifications for Exponential Random Graph Models
Article Snippet: The most promising class of statistical models for expressing structural properties of social networks observed at one moment in time is the class of exponential random graph models (ERGMs), also known as p ∗ models.. The strong point of these models is that they can represent a variety of structural tendencies, such as transitivity, that define complicated dependence patterns not easily modeled by more basic probability models.. Recently, Markov chain Monte Carlo (MCMC) algorithms have been developed that produce approximate maximum likelihood estimators.

Article Title: Instability, Sensitivity, and Degeneracy of Discrete Exponential Families
Article Snippet: In terms of MCMC, the nearest neighborhood log odds |Λ N ( x N , y N ; θ )| are O ( n ), which suggests that MCMC algorithms tend to suffer from extremely slow mixing, as is well-known ( Snijders 2002 , Handcock 2002a , 2003a ).

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Article Title: ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks
Article Snippet: .. Snijders (2002) reports on some odd convergence properties of the MCMC algorithms described here for particular choices of an ERGM and a parameter vector. ..



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