# MCMC methods

### Classification of chirp signals using hierarchical bayesian learning and MCMC methods

**MCMC**

**methods**, which are widely used for Bayesian estimation, are also a suitable tool for su- pervised classification using hierarchical Bayesian ...with

**MCMC**

**methods**was ...

12

### Classification of linear and non-linear modulations using the Baum–Welch algorithm and MCMC methods

15

### Blind marine seismic deconvolution using statistical MCMC methods

12

### Blind marine seismic deconvolution using statistical MCMC methods

**MCMC**method gives a better deconvolution than the ML ...by

**MCMC**is more accurate with less misses and false detection than by ML, where more false detections ...both

**methods**show good robustness ...

11

### Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems using MCMC

**MCMC**

**methods**requires a step of drawing samples from a high dimensional Gaussian distribution. While direct Gaussian ...

21

### Stochastic thermodynamic integration: efficient Bayesian model selection via stochastic gradient MCMC

**methods**have lost their charm in various machine learning appli- cations especially during the last decade, as they are perceived to be computationally very ...the

**methods**impractical even for ...

6

### Hierarchical multispectral galaxy decomposition using a MCMC algorithm with multiple temperature simulated annealing

**MCMC**

**methods**.

**MCMC**algorithms allow to sample the parameter space according to the target distribution and theoretical results prove the convergence of the distribution of the samples to the ...

28

### MCMC design-based non-parametric regression for rare-event. Application to nested risk computations

27

### Relabelling MCMC Algorithms in Bayesian Mixture Learning

**MCMC**) sampling has demonstrated to be a powerful and versatile method for Bayesian inference ...

3

### Controlled MCMC for Optimal Sampling

38

### Bayesian multi-locus pattern selection and computation through reversible jump MCMC

33

### Average of Recentered Parallel MCMC for Big Data

**MCMC**

**methods**, such as Metropolis- Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the whole data set at each ...rescue

**MCMC**...

14

### Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

**MCMC**

**methods**cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts ...an

**MCMC**method using an ABC ...

15

### Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC

13

### Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

**methods**can provide significant empirical performance improve- ments, they tend either to over- or under-utilize the surrogate, sacrificing exact sampling or potential speedup, ...many

**methods**...

57

### Information bounds and MCMC parameter estimation for the pile-up model

**MCMC**...

20

### Design Methods

68

### Perturbation Methods

**methods**≡ all parameters of order 1, all sizes and dimensions of the same order ...perturbation

**methods**≡ small parameter • what is small ? (dimensional analysis) • very small means very ...

32

### Building Detection by Markov Object processes and a MCMC Algorithm

35

### Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

39