Description: gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation.
It is a very good surce code for intelligent reasoning research-gibbs, beyesian network, intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research Platform: |
Size: 27211 |
Author:程红 |
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Description: gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation.
It is a very good surce code for intelligent reasoning research-gibbs, beyesian network, intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research Platform: |
Size: 27648 |
Author:程红 |
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Description: 本源码是基于Markov chain Monte Carlo (MCMC)的Bayesian inference工具包,其中包括MCMC采样，基于MCMC的高斯分类，同时描述了采样的一些方法。其中还有使用文档-toolbox is a collection of Matlab functions for Bayesian inference
with Markov chain Monte Carlo (MCMC) methods Platform: |
Size: 11885568 |
Author:吴晓明 |
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Description: This book provides the first unified, up-to-date, and tutorial-level overview of sequence
analysis methods, with particular emphasis on probabilistic modelling. Pairwise alignment,
hidden Markov models, multiple alignment, profile searches, RNA secondary structure
analysis, and phylogenetic inference are treated at length. Platform: |
Size: 3951616 |
Author:Dang Tran Vu |
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Description: The book: Inference in Hidden Markov Models. This is the basic book about HMM. The book starts with an introductory chapter which explains, in simple terms, what an HMM is, and it contains many examples of the use of HMMs
in fields ranging from biology to telecommunications and finance Platform: |
Size: 5383168 |
Author:Dang Tran Vu |
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Description: 隐马尔可夫模型（HMM）在现实中处处存在，且对于推断和预测很有指导意义，其理论比较艰深，这是一个浅显易懂的浙江大学的讲稿，希望对大家有所帮助。-Hidden Markov Model (HMM) in reality is all about, and for inference and forecasting is very instructive, its theoretical comparison difficult, it is an easy to understand the speech of Zhejiang University, I hope all of us help. Platform: |
Size: 79872 |
Author:fanyx |
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Description: 这是国外研究Gibbs采样和Bayesian推理的研究人员写的工具包软件，最新版本为V1.4.3。很适合研究机器学习及其贝叶斯推理的科研人员使用。-The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. The project began in 1989 in the MRC Biostatistics Unit and led initially to the `Classic BUGS program, and then onto the WinBUGS software developed jointly with the Imperial College School of Medicine at St Mary s, London. Development now also includes the OpenBUGS project in the University of Helsinki, Finland. There are now a number of versions of BUGS, which can be confusing. Platform: |
Size: 11234304 |
Author:王磊 |
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Description: 本程序是基于马尔可夫链蒙特卡尔理论的贝叶斯工具。版本为MCMC Methods for MLP and GP and Stuff (for Matlab) V2.1 -A collection of matlab functions for Bayesian inference with Markov chain Monte Carlo (MCMC) methods. The purpose of this toolbox was to port some of the features in fbm to matlab for easier development for matlab users.
Platform: |
Size: 11837440 |
Author:hu |
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Description: 置信传播立体匹配算法，详细说明消息传递的过程，经典论文！-Markov random ?eld models provide a robust and uni?ed framework for early vision problems such as stereo and image restoration. Inference algorithms based on graph cuts and belief propagation have been found to yield accurate results, but despite recent advances are often too slow for practical use. Platform: |
Size: 189440 |
Author:mstar |
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Description: This a reference implementation for the synthetic experiments on lower
linear envelope inference and learning described in
"Max-margin Learning for Lower Linear Envelope Potentials in Binary
Markov Random Fields", Stephen Gould, ICML 2011.-This is a reference implementation for the synthetic experiments on lower
linear envelope inference and learning described in
"Max-margin Learning for Lower Linear Envelope Potentials in Binary
Markov Random Fields", Stephen Gould, ICML 2011. Platform: |
Size: 103424 |
Author:newmerce |
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Description: libDAI is a free/open source C++ library that provides implementations of
various (approximate) inference methods for discrete graphical models. libDAI
supports arbitrary factor graphs with discrete variables this includes
discrete Markov Random Fields and Bayesian Networks-libDAI is a free/open source C++ library that provides implementations of
various (approximate) inference methods for discrete graphical models. libDAI
supports arbitrary factor graphs with discrete variables this includes
discrete Markov Random Fields and Bayesian Networks Platform: |
Size: 1812480 |
Author:newmerce |
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Description: Alchemy: a tool in c++ for markov logic network inference, parameter learning and structure learning
Platform: |
Size: 12003328 |
Author:xyz1712 |
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Description: 此工具箱支持推理和学习HMM模型,拥有的算法有离散输出（DHMM），高斯输出（GHMM），或其混合物的高斯输出（mhmm）。-Hidden Markov Model (HMM) Toolbox for Matlab，This toolbox supports inference and learning for HMMs with discrete outputs (dhmm s), Gaussian outputs (ghmm s), or mixtures of Gaussians output (mhmm s). The Gaussians can be full, diagonal, or spherical (isotropic). It also supports discrete inputs, as in a POMDP. The inference routines support filtering, smoothing, and fixed-lag smoothing. Platform: |
Size: 409600 |
Author:Bill |
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Description: This the MATLAB code that was used to produce the figures and tables in Section V of
F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference
in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on Image
Processing, 2006.
1
MATLAB has the capability of running functions written in C. The files which hold the source
for these functions are called MEX-Files. Some functions of our codes are written in C.
The purpose of this software is to implement the MCVEM algorithm, described in the paper
mentioned above, when applied to Image Segmentation. MCVEM consists in combining approximation
techniques - based on variational EM - and simulation techniques - based on MCMC
-.
This software is the first version that is made publicly available.
2 How to
2.1 Obtain the source code
Download it from
http://www.tsi.enst.fr/gfort/INRIA/MCVEM.html
After unpacking the archive, you should obtain
• two-This is the MATLAB code that was used to produce the figures and tables in Section V of
F. Forbes and G. Fort, Combining Monte Carlo and mean-field like methods for inference
in Hidden Markov Random Fields, Accepted for publication in IEEE Trans. on Image
Processing, 2006.
1
MATLAB has the capability of running functions written in C. The files which hold the source
for these functions are called MEX-Files. Some functions of our codes are written in C.
The purpose of this software is to implement the MCVEM algorithm, described in the paper
mentioned above, when applied to Image Segmentation. MCVEM consists in combining approximation
techniques - based on variational EM - and simulation techniques - based on MCMC
-.
This software is the first version that is made publicly available.
2 How to
2.1 Obtain the source code
Download it from
http://www.tsi.enst.fr/gfort/INRIA/MCVEM.html
After unpacking the archive, you should obtain
• two Platform: |
Size: 692224 |
Author:jeevithajaikumar |
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Description: In this paper we present Discriminative Random Fields (DRF), a discrim- inative framework for the classification of natural image regions by incor- porating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classifica- tion problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.
Platform: |
Size: 151552 |
Author:asdf12341234 |
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Description: An implementation of forward–backward algorithm. This algorithm is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations/emissions. Platform: |
Size: 1024 |
Author:lalaoui |
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Description: 该方法实现了一种实时的由粗到细的超像素分割,是cvpr2015的一篇paper，该方法的效果非日常的好。指得大家学习和借鉴。-In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is
on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random
field. We propose a coarse to fine optimization technique
that speeds up inference in terms of the number of updates
by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We uate
and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach
significantly outperforms the baselines in the segmentation
metrics and achieves the lowest error on the stereo task. Platform: |
Size: 1222656 |
Author:张丽霞 |
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Description: 《人工智能:一种现代方法(第2版)》既详细介绍了大量的基本概念、思想和算法，也描述了各研究方向最前沿的进展，同时收集整理了详实的历史文献与事件。因此《人工智能:一种现代方法(第2版)》适合于不同层次和领域的研究人员及学生，可以作为信息领域和相关领域的高等院校本科生和研究生的教材或教学辅导书目，也可以作为相关领域的科研与工程技术人员的参考书。-In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI. Platform: |
Size: 31363072 |
Author:三木牛 |
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Search - Markov inference