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MCMC Variational Inference via Uncorrected Hamiltonian Annealing
[article]
2021
arXiv
pre-print
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We propose a framework to use an AIS-like procedure with Uncorrected Hamiltonian MCMC, called Uncorrected Hamiltonian Annealing. Our method leads to tight and differentiable lower bounds on log Z. ...
Annealed Importance Sampling (AIS) with Hamiltonian MCMC is a powerful method that can be used to do this. ...
Uncorrected Hamiltonian Annealing The contribution of this paper is the development of uncorrected Hamiltonian Annealing (UHA). ...
arXiv:2107.04150v3
fatcat:khtfbclymve6zfc575tb6m5jue
Ergodic variational flows
[article]
2022
arXiv
pre-print
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This work presents a new class of variational family -- ergodic variational flows -- that not only enables tractable i.i.d. sampling and density evaluation, but also comes with MCMC-like convergence guarantees ...
Further, we develop a particular instantiation of the general family using Hamiltonian dynamics combined with deterministic momentum refreshment. ...
One can view T λ as being similar to a single iteration of uncorrected Hamiltonian Monte Carlo (HMC) [66, 69] . ...
arXiv:2205.07475v1
fatcat:jlgkhdynorcyjdz4r5qbxac6oq
Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data
2019
Entropy
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Using artificial data, we show that a Potts model inferred using these corrected frequencies performs better in predicting contacts and fitness effect of mutations. ...
Here, we propose an inference scheme that takes the phylogeny of a protein family into account in order to correct biases in estimating the frequencies of amino acids. ...
finite-time MCMC run. ...
doi:10.3390/e21111090
fatcat:vsvaxnza5jdplpx27qrhz3tyd4
Generalized Normalizing Flows via Markov Chains
[article]
2022
arXiv
pre-print
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Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This chapter provides a unified framework to handle these approaches via Markov chains. ...
The Markov chains point of view enables us to couple both deterministic layers as invertible neural networks and stochastic layers as Metropolis-Hasting layers, Langevin layers, variational autoencoders ...
Hamiltonian steps. ...
arXiv:2111.12506v3
fatcat:lpgdigzixfa5po6li2zarg6gey
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
[article]
2022
arXiv
pre-print
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However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. ...
Monte Carlo HMC Two-step MCMC | simulates Hamiltonian dynamics 4.1 All All All 6.5.1 Langevin dynamics LD One-step MCMC | stochastic gradient descent with added noise 4.1 6.1.1 Mean-field variational ...
Similarly to posterior tempering in MCMC techniques, the optimal value of τ can be obtained via cross-validation. Factorized Gaussian as a variational distribution. ...
arXiv:2201.07766v1
fatcat:rlu4mn4rrbap5maav7k4wdauh4
Convergence and evolving amino acid propensities
[article]
2021
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Another key feature of our thermodynamic model is that at its base is a Hamiltonian-Potts model, i.e., an energy model. ...
V.5.3 Method Validation via Simulations De Novo Simulations The de novo simulations where sequences were generated directly from the model (Simulation 1) shows that the correct acceptabilities can be inferred ...
How did I convert the Linear Fit MCMC from Perl to OO C++? How did I just convert my perl script into C++? ...
doi:10.25677/r4rj-3t27
fatcat:gra4ld3euvga7frmye3vvdopym
Modern Applied Science, Vol. 3, No. 4, April 2009, all in one file
2009
Modern Applied Science
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So, an expression for ( ) Var ϕ can be derived via the delta method. Below we consider a MCMC-aided Bayesian approach. ...
Experiment
General Melting points were determined on a Fisher Johns micro-melting point apparatus and are uncorrected. ...
doi:10.5539/mas.v3n4p0
fatcat:zy26n4n5wjhwjktpcw4jtbs7fm