How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis Mauno Vihinen BMC Genomics 2012, 13 (Suppl 4):S2 doi:10.1186/1471-2164-13-S4-S2 http://www.biomedcentral.com/1471-2164/13/S4/S2 This checklist is provided to help when comparing and measuring performance of predictors and when selecting a suitable one. These are items that method developers should include in articles, or as supplement to articles, as they enable effective comparison and evaluation of the performance of predictors. Items to check when estimating method performance and comparing performance of different methods: - Is the method described in detail? - Have the developers used established databases and benchmarks for training and testing (if available)? - If not, are the datasets available? - Is the version of the method mentioned (if several versions exist)? - Is the contingency table available? - Have the developers reported all the six performance measures: sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. If not, can they be calculated from figures provided by developers? - Has cross validation or some other partitioning method been used in method testing? - Are the training and test sets disjoint? - Are the results in balance e.g. between sensitivity and specificity? - Has the ROC curve been drawn based on the entire test set? - Inspect the ROC curve and AUC. - How does the method compare to others in all the measures? - Does the method provide probabilities for predictions?
4th WGNE workshop on systematic errors in weather and climate models The JSC/CAS Working Group on Numerical Experimentation (WGNE) is organising a workshop on systematic errors in weather and climate models to be hosted at the Met Office, Exeter, UK, during 15-19 April 2013. The principal goal will be to increase understanding of the nature and cause of errors in models used for weather and climate prediction (including intra-seasonal to inter-annual). It is anticipated that the focus will be on General Circulation Models (GCMs) such as those used in CMIP5 , TIGGE , operational NWP, etc., including atmosphere-only, coupled atmosphere-ocean and earth system models. Biases in the atmosphere, land surface, ocean and cryosphere are all of interest. A wide variety of diagnostic techniques will be discussed, including traditional analysis methods applied to global models, process studies, the use of diagnostic and process models (e.g. single-column, cloud-resolving), and simplified experiments (e.g. aqua-planet). Of special interest will be studies that consider errors found in multiple models and errors which are present across timescales. Diagnostics and metrics that utilize novel or multi-variate observational resources and constraints to identify and characterize systematic errors are welcomed, together with studies which infer the amount of systematic error in predicted extremes from systematic errors in non-extreme situations. Alongside WGNE , the following groups will contribute to the coordination of the workshop: The Working Group on Coupled Models ( WGCM ), the Working Group on Seasonal to Inter-annual Prediction ( WGSIP ), the Working Group on Ocean Model Development ( WGOMD ), Stratospheric Processes And their Role in Climate ( SPARC ), Global Energy and Water Cycle Experiment ( GEWEX ), the Joint Working Group on Forecast Verification Research ( JWGFVR ), and the Year Of Tropical Convection ( YOTC ) project. 详细信息见：http://www.metoffice.gov.uk/conference/wgne2013
2011JD017069.pdf Citation: Yong, B. , Y. Hong, L. L. Ren, , J. J. Gourley, G. J. Huffman, X. Chen, W. Wang, and S. I. Khan (2012), Assessment of evolving TRMM-based multi-satellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin, Journal of Geophysical Research- Atmosphere , 117, D09108, doi: 10.1029/2011JD017069 .
Abstract: hort-term earthquake prediction has always been a very difficult problem in geology, 15 this article pre-displacement, pre-established short-term break for the earthquake prediction based on the theory becomes completely abandoned to form the basis of earthquake prediction method, short-term earthquake prediction is a theoretical breakthrough. Key words: Mechanics; earthquake,;short-term forecasting,;pre-displacement; pre-fracture 摘要： 地震短期预报历来是一个十分困难的地质学问题，本文以预位移预断裂为依据对于短期地震预报进行了理论思考，一旦该理论被实践所证明，将会是地震短期预报的一次理论突破。 关键词 ：固体力学；地震；短期预报；预位移；预断裂 预位移预断裂短期地震预报数学方法探析.pdf
基于网络的 预测13347.full.pdf Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis Lisa Phama, Lisa Christadoreb, Scott Schausb, and Eric D. Kolaczykc,1 aProgram in Bioinformatics, Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichmentbased methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.
Why? Because the price for houses will go up eventually! Gary Shilling: 20% Drop in Housing to Cause Recession in 2012 Gary Shilling, President of A. Gary Shilling Co. and author of the Age of Deleveraging says another recession is brewing -- no matter what action the Fed takes. Shilling says the shock to trigger the next recess is "another big leg-down in housing." (An asset class the Fed has not been able to reflate.) As those familiar with Shilling know, his forecasts are generally bearish. However, in his defense, Shilling was one of the few economists who correctly predicted the dangers of the subprime mortgage market and its impact on the broader economy. The problem with the real estate market remains excess inventory. Based on Shilling's research, there are 2 million to 2.5 million excess homes in the country -- a supply that will take 4-5 years to work-off. The result: Housing prices will fall another 20% and underwater mortgages will balloon from 23% to 40%, he says. With housing slumping again, Shilling says recession is coming to a town near you in 2012. http://finance.yahoo.com/blogs/daily-ticker/20-drop-housing-cause-recession-2012-says-gary-161445494.html
Isaac Newton Institute for Mathematical Sciences Mathematical and Statistical Approaches to Climate Modelling and Prediction 11 August - 22 December 2010 Here is the link: http://www.newton.ac.uk/programmes/CLP/index.html
Many complex systems can be well described by networks where nodes present individuals or agents, and links denote the relations or interactions between nodes. Recently, the link prediction of complex networks has attracted more and more attention from computer scientists and physicists. Link prediction aims at estimating the likelihood of the existence of a link between two nodes, based on the observed links and the attributes of the nodes. For example, classical information retrieval can be viewed as predicting missing links between words and documents, and the process of recommending items to a user can be considered as a link prediction problem in the user-item bipartite network. Attached please find two newly published papers about the problem of link prediction. One (EPJB)discussed missing links prediction via local information. The other (PRE) introduced an efficient and effective similarity index, called Local Path index for link prediction. PRE_80_046122 EPJB_71_623
标签: prediction
