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Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/25
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
A New Proof of Convergence of MCMC via the Ergodic Theorem
Markov chain Monte Carlo Harris recurrence η-irreducibility
2015/7/6
A key result underlying the theory of MCMC is that any η-irreducible Markov chain having a transition density with respect to η and possessing a stationary distribution is automatically positive Harri...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data
Bayesian modeling case-control design data augmentation logistic regres-sion Markov Chain Monte Carlo population prevalence presence-only data simulation
2013/6/13
Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work w...
MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Sparse graphical model Reversible Markov chain Markov equivalence class
2013/4/27
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov eq...
Fast MCMC sampling for Markov jump processes and extensions
Markov jump process uniformization MCMC Gibbs sampler Markov-modulated Poisson process continuous-time Bayesian network
2012/9/17
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simu-lating from the posterior d...
基于分层转移的粒子滤波MCMC重采样算法
Monte Carlo Markov 链 重采样 粒子滤波
2014/9/25
针对粒子滤波中如何设计重采样策略以解决“权值蜕化”, 同时又可避免“样本贫化” 的问题, 提出一种基于分层转移的Monte Carlo Markov 链(MCMC) 重采样算法. 当样本容量检测出现“蜕化” 时, 将样本集按权值蜕化程度进行分层, 利用提出的变异繁殖算法, 将其与PSO 融合产生MCMC转移核, 并施以分层子集; 然后通过Metroplis-Hastings 算法进行接收-拒绝采样...
Nonasymptotic bounds on the estimation error of MCMC algorithms
Mean square error Computable bounds
2011/7/6
We address the problem of upper bounding the mean square error of MCMC estimators. Our analysis is non-asymptotic.
基于MCMC方法的中国期货市场流动性研究
MCMC方法 流动性 期货市场
2012/3/19
使用广义序贯交易模型,基于高频的逐笔交易数据(交易价格和成交量),对中国期货市场的流动性进行了研究.由于在广义序贯交易模型中需要考虑交易发起的方向和资产的有效价格等无法观测的变量,因此借助MCMC的统计抽样方法在贝叶斯统计的框架下对模型的参数进行估计.主要考察了中国期货市场上具有代表性的7个期货合约,结果显示:从交易成本和交易对于有效价格的影响系数这两个指标来看,黄金期货的流动性最强;如果仅考虑交...
针对已有的信用溢价模型只考虑了单一尺度的波动均值回复过程,从而导致的信息损失问题,提出了贝叶斯复合状态信用溢价模型,据此辨析不同尺度的信用溢价波动回复状态。利用不同剩余偿付期的中国企业债信用溢价指数序列,引入了基于混合正态分布的多步MCMC方法对复合状态模型进行贝叶斯分析,研究结果表明:不同剩余偿付期的债券具有不同的异方差水平,均值回复过程可以区分为长期和短期两种趋势,并且分别具有不同的尺度维度;...
Importance Re-sampling MCMC for Cross-Validation in Inverse Problems
Cross-validation Inverse Importance Re-sampling Model fit Re-use
2009/9/22
This paper presents a methodology for cross-validation in the context of Bayesian
modelling of situations we loosely refer to as iverse problems It is motivated by
an example from palaeoclimatology ...
Construction of weakly CUD sequences for MCMC sampling
completely uniformly distributed Gibbs sampler equidistribution probit quasi-Monte Carlo
2009/9/16
In Markov chain Monte Carlo (MCMC) sampling considerable thought goes into constructing random transitions. But those transitions are almost always driven by a simulated IID sequence. Recently it has ...
This paper surveys various results about Markov chains on general (non-countable) state spaces. It begins with an introduction to Markov chain Monte Carlo (MCMC) algorithms, which provide the motivati...