Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Surjanovic, Nikola"'
Autor:
Liu, Tiange, Surjanovic, Nikola, Biron-Lattes, Miguel, Bouchard-Côté, Alexandre, Campbell, Trevor
Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selecting an appropriate step size is often a challenging task in practice; and for complex multiscale targets
Externí odkaz:
http://arxiv.org/abs/2410.18929
Autor:
Luu, Son, Xu, Zuheng, Surjanovic, Nikola, Biron-Lattes, Miguel, Campbell, Trevor, Bouchard-Côté, Alexandre
The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domination of HMC for the Bayesian analysis of GLMs. By utilizing the struc
Externí odkaz:
http://arxiv.org/abs/2410.03630
We demonstrate that adaptively controlling the size of individual regression trees in a random forest can improve predictive performance, contrary to the conventional wisdom that trees should be fully grown. A fast pruning algorithm, alpha-trimming,
Externí odkaz:
http://arxiv.org/abs/2408.07151
Non-reversible parallel tempering (NRPT) is an effective algorithm for sampling from target distributions with complex geometry, such as those arising from posterior distributions of weakly identifiable and high-dimensional Bayesian models. In this w
Externí odkaz:
http://arxiv.org/abs/2405.11384
This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includ
Externí odkaz:
http://arxiv.org/abs/2402.09598
Autor:
Biron-Lattes, Miguel, Surjanovic, Nikola, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for an arbitrary target distribution can be a difficult task and even the b
Externí odkaz:
http://arxiv.org/abs/2310.16782
Autor:
Surjanovic, Nikola, Biron-Lattes, Miguel, Tiede, Paul, Syed, Saifuddin, Campbell, Trevor, Bouchard-Côté, Alexandre
We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimodal posteriors arising in Bayesian inference and high-dimensional distri
Externí odkaz:
http://arxiv.org/abs/2308.09769
Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating
Externí odkaz:
http://arxiv.org/abs/2206.00080
Autor:
Surjanovic, Nikola, Loughin, Thomas M.
The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from simulations showing that the type 1 error rate (and he
Externí odkaz:
http://arxiv.org/abs/2102.12698
Generalized linear models (GLMs) are used within a vast number of application domains. However, formal goodness of fit (GOF) tests for the overall fit of the model$-$so-called "global" tests$-$seem to be in wide use only for certain classes of GLMs.
Externí odkaz:
http://arxiv.org/abs/2007.11049