Search Results for author: Akihiko Nishimura

Found 5 papers, 4 papers with code

Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates

2 code implementations6 Nov 2019 Akihiko Nishimura, Marc A. Suchard

Use of continuous shrinkage priors -- with a "spike" near zero and heavy-tails towards infinity -- is an increasingly popular approach to induce sparsity in parameter estimates.

Methodology Statistics Theory Statistics Theory

Gradients do grow on trees: a linear-time ${\cal O}\hspace{-0.2em}\left( N \right)$-dimensional gradient for statistical phylogenetics

1 code implementation29 May 2019 Xiang Ji, Zhen-Yu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A. Suchard

To make this tractable, we present a linear-time algorithm for ${\cal O}\hspace{-0. 2em}\left( N \right)$-dimensional gradient evaluation and apply it to general continuous-time Markov processes of sequence substitution on a phylogenetic tree without a need to assume either stationarity or reversibility.

Computation Populations and Evolution Methodology

Prior-preconditioned conjugate gradient method for accelerated Gibbs sampling in "large $n$ & large $p$" Bayesian sparse regression

2 code implementations29 Oct 2018 Akihiko Nishimura, Marc A. Suchard

We can then solve the linear system by the conjugate gradient (CG) algorithm through matrix-vector multiplications by $\Phi$; this involves no explicit factorization or calculation of $\Phi$ itself.

Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods

1 code implementation23 May 2017 Akihiko Nishimura, David Dunson, Jianfeng Lu

Hamiltonian Monte Carlo has emerged as a standard tool for posterior computation.


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