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Found 18 papers, 7 papers with code
A Probabilistic Approach to Language Change
We present a probabilistic approach to language change in which word forms are represented by phoneme sequences that undergo stochastic edits along the branches of a phylogenetic tree.
Efficient Inference in Phylogenetic InDel Trees
Accurate and efficient inference in evolutionary trees is a central problem in computational biology.
Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs
Pruning can massively accelerate the computation of feature expectations in large models.
Variational Inference over Combinatorial Spaces
Since the discovery of sophisticated fully polynomial randomized algorithms for a range of #P problems (Karzanov et al., 1991; Jerrum et al., 2001; Wilson, 2004), theoretical work on approximate inference in combinatorial spaces has focused on Markov chain Monte Carlo methods.
Bayesian Pedigree Analysis using Measure Factorization
Meanwhile, analysis methods have remained limited to pedigrees of <100 individuals which limits analyses to many small independent pedigrees.
Entangled Monte Carlo
We propose a novel method for scalable parallelization of SMC algorithms, Entangled Monte Carlo simulation (EMC).
An Entropy Search Portfolio for Bayesian Optimization
How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i. e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance.
Divide-and-Conquer with Sequential Monte Carlo
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.
Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning.
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
We explore and propose several original extensions of an alternative approach introduced recently in Peters and de With (2012) where the target distribution of interest is explored using a continuous-time Markov process.
Methodology Statistics Theory Statistics Theory
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like $\Theta(n)$ in the number of data points $n$.
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler
An important aspect of the practical implementation of BPS is the simulation of event times.
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua
Blang allows users to perform Bayesian analysis on arbitrary data types while using a declarative syntax similar to BUGS.
Particle-Gibbs Sampling For Bayesian Feature Allocation Models
To overcome this problem we have developed a Gibbs sampler that can update an entire row of the feature allocation matrix in a single move.
Slice Sampling for General Completely Random Measures
Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that influence the data grows with the size of the data set.
Parallel Tempering on Optimized Paths
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target, and then interchanges states along the path to improve mixing in the target.
Computation 65C05
MCMC-driven learning
This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo.
