Hidden Agendas in Beratungs-und Veränderungsprozessen

Consulting and organization development claim to boost the client organization’s efficiency — this official goal is frequently foiled by hidden agendas which are not officially communicated. Hidden functions of consulting are to prove to the management’s legitimacy, to explain the failure of change processes, to strengthen the initator’s position in internal power struggles or to relieve the system from conflict. On the consultants’ side there is a tension between professionalism on one side and the necessity to secure follow-up projects and to sell standard products on the other side. The authors present possible interpretations for such hidden agendas and illustrate them with case studies from their practical experience as consultants.     

A non-homogeneous hidden Markov model for precipitation occurrence

A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the so-called 'downscaling problem'). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.     

服务周边地区发展的福建省物流发展思路

国务院颁布的《关于支持福建省加快建设海峡西岸经济区的若干意见》,把建设服务周边地区发展新的对外开放综合通道作为赋予海峡西岸经济区四大战略定位之一。这一定位从一定意义上来说,就是福建省要先行先试,充分发挥福建省作为海峡西岸经济区建设的主体作用和比较优势,在海峡西岸区域经济合作基础上,大力发展物流,通过建设物流运输网络、物流合作平台和物流节点网络,积极推动跨区域物流合作,形成服务中西部发展新的重要物流通道,拓展区域合作的空间,使福建成为广东、浙江、江西等周边省份和湖南、安徽等中西部省份扩大对外开放、加快经济发展的有力平台。     

MML Markov classification of sequential data

General purpose un-supervised classification programs have typically assumed independence between observations in the data they analyse. In this paper we report on an extension to the MML classifier Snob which enables the program to take advantage of some of the extra information implicit in ordered datasets (such as time-series). Specifically the data is modelled as if it were generated from a first order Markov process with as many states as there are classes of observation. The state of such a process at any point in the sequence determines the class from which the corresponding observation is generated. Such a model is commonly referred to as a Hidden Markov Model. The MML calculation for the expected length of a near optimal two-part message stating a specific model of this type and a dataset given this model is presented. Such an estimate enables us to fairly compare models which differ in the number of classes they specify which in turn can guide a robust un-supervised search of the model space. The new program, tSnob, is tested against both synthetic data and a large real world dataset and is found to make unbiased estimates of model parameters and to conduct an effective search of the extended model space.     

智能生命信息无线传感器低功耗研究

介绍了无线智能生命信息传感器节点和网络的概念,从数据采集和计算、数据传输、硬件及软件方面对无线生命信息传感器节点和网络的节能进行了比较研究。     

国际贸易碳转移的全球图景和时间演变

在环境扩展的全球多区域投入产出（EE-MRIO）模型框架下,本文基于WIOD数据库,使用EEBT和MRIO两种方法核算了全球7个区域的生产碳排放、消费碳足迹、国际贸易隐含碳以及净碳转移,揭示了国际贸易碳转移的全球图景和时间演变。研究结果为国际气候谈判和全球气候变化应对提供了进一步的信息和启示。     

Following a moving target—Monte Carlo inference for dynamic Bayesian models

Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration.     

Large-scale multiple testing under dependence

Summary.  The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal data-driven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively exploiting the dependence structure among hypotheses, and hence conventional FDR procedures that ignore this structural information are inefficient. Both theoretical properties and numerical performances of the procedures proposed are investigated. It is shown that the procedures proposed control FDR at the desired level, enjoy certain optimality properties and are especially powerful in identifying clustered non-null cases. The new procedure is applied to an influenza-like illness surveillance study for detecting the timing of epidemic periods.     

Statistical Inference for Partially Hidden Markov Models

ABSTRACT

In this article we introduce a new missing data model, based on a standard parametric Hidden Markov Model (HMM), for which information on the latent Markov chain is given since this one reaches a fixed state (and until it leaves this state). We study, under mild conditions, the consistency and asymptotic normality of the maximum likelihood estimator. We point out also that the underlying Markov chain does not need to be ergodic, and that identifiability of the model is not tractable in a simple way (unlike standard HMMs), but can be studied using various technical arguments.