Parallel Slice Sampling

2013 ◽  
pp. 81-84 ◽  
Author(s):  
Teresa Pietrabissa ◽  
Simone Rusconi
Keyword(s):  
Slice Sampling

scholarly journals Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 642 ◽  
Author(s):  
Erlandson Saraiva ◽  
Adriano Suzuki ◽  
Luis Milan
Keyword(s):  
Computational Methods ◽  
Posterior Distribution ◽  
Estimation Procedure ◽  
Small Sample ◽  
Survival Model ◽  
Data Set ◽  
Right Censored Data ◽  
Slice Sampling ◽  
Bivariate Survival ◽  
Model Based

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis–Hastings algorithm: Independent Metropolis–Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis–Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be difficult, we also describe how to update a parameter of interest using the slice sampling (SS) method. A simulation study was carried out to compare the performances of the IMH, RWM and SS. A comparison was made using the sample root mean square error as an indicator of performance. Results obtained from the simulations show that the SS algorithm is an effective alternative to the IMH and RWM methods when simulating values from the posterior distribution, especially for small sample sizes. We also applied these methods to a real data set.

Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram

2018 ◽  
Vol 45 (10) ◽  
pp. 1004001
Author(s):  
佟国峰 Tong Guofeng ◽  
杜宪策 Du Xiance ◽  
李勇 Li Yong ◽  
陈槐嵘 Chen Huairong ◽  
张庆春 Zhang Qingchun
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Slice Sampling ◽  
Cloud Classification ◽  
Centroid Distance ◽  
Outdoor Scenes ◽  
Vertical Slice ◽  
Point Cloud Classification ◽  
Distance Histogram

scholarly journals Gibbs-Slice Sampling Algorithm for Estimating the Four-Parameter Logistic Model

2020 ◽  
Vol 11 ◽  
Author(s):  
Jiwei Zhang ◽  
Jing Lu ◽  
Hang Du ◽  
Zhaoyuan Zhang
Keyword(s):  
Logistic Model ◽  
Sampling Algorithm ◽  
Slice Sampling

scholarly journals Vehicle tracking and classification in challenging scenarios via slice sampling

2011 ◽  
Vol 2011 (1) ◽  
Author(s):  
Marcos Nieto ◽  
Luis Unzueta ◽  
Javier Barandiaran ◽  
Andoni Cortés ◽  
Oihana Otaegui ◽  
...  
Keyword(s):  
Vehicle Tracking ◽  
Slice Sampling

Multivariate Normal Slice Sampling

2010 ◽  
Vol 19 (2) ◽  
pp. 281-294 ◽  
Author(s):  
Merrill W. Liechty ◽  
Jingjing Lu
Keyword(s):  
Multivariate Normal ◽  
Slice Sampling

scholarly journals Slice Sampling Particle Belief Propagation

2013 ◽  
Author(s):  
Oliver Muller ◽  
Michael Ying Yang ◽  
Bodo Rosenhahn
Keyword(s):  
Belief Propagation ◽  
Slice Sampling

Graphical Model MAP Inference with Continuous Label Space in Computer Vision

2018 ◽  
Author(s):  
Oliver Müller
Keyword(s):  
Graphical Model ◽  
Belief Propagation ◽  
Fundamental Problem ◽  
Parameter Tuning ◽  
3D Models ◽  
Target Object ◽  
State Variables ◽  
Slice Sampling ◽  
Global Consistency ◽  
Continuous State

This thesis deals with monocular object tracking from video sequences. The goal is to improve tracking of previously unseen non-rigid objects under severe articulations without relying on prior information such as detailed 3D models and without expensive offline training with manual annotations. The proposed framework tracks highly articulated objects by decomposing the target object into small parts and apply online tracking. Drift, which is a fundamental problem of online trackers, is reduced by incorporating image segmentation cues and by using a novel global consistency prior. Joint tracking and segmentation is formulated as a high-order probabilistic graphical model over continuous state variables. A novel inference method is proposed, called S-PBP, combining slice sampling and particle belief propagation. It is shown that slice sampling leads to fast convergence and does not rely on hyper-parameter tuning as opposed to competing approaches based on Metropolis-Hastings or heuristi...

Slice sampling for simulation based fitting of spatial data models

2005 ◽  
Vol 15 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Deepak K. Agarwal ◽  
Alan E. Gelfand
Keyword(s):  
Spatial Data ◽  
Data Models ◽  
Slice Sampling ◽  
Simulation Based ◽  
Spatial Data Models
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