A Hidden-State Maximum Entropy Model Forword Confidence Estimation

Empirical evidence of scaling behaviors in neuronal avalanches suggests that neuronal populations in the brain operate near criticality. Departure from scaling in neuronal avalanches has been used as a measure of distance to criticality and linked to brain disorders. A distinct line of evidence for brain criticality has come from thermodynamic signatures in maximum entropy (ME) models. Both of these approaches have been widely applied to the analysis of neuronal data. However, the relationship between deviations from avalanche criticality and thermodynamics of ME models of neuronal populations remains poorly understood. To address this question, we study spontaneous activity of organotypic rat cortex slice cultures in physiological and drug-induced hypo- or hyper-excitable conditions, which are classified as critical, subcritical and supercritical based on avalanche dynamics. We find that ME models inferred from critical cultures show signatures of criticality in thermodynamic quantities, e.g. specific heat. However, such signatures are also present, and equally strong, in models inferred from supercritical cultures—despite their altered dynamics and poor functional performance. On the contrary, ME models inferred from subcritical cultures do not show thermodynamic hints of criticality. Importantly, we confirm these results using an interpretable neural network model that can be tuned to and away from avalanche criticality. Our findings indicate that maximum entropy models correctly distinguish subcritical from critical/supercritical systems. However, they may not be able to discriminate between avalanche criticality and supercriticality, although they may still capture a number of important features from neuronal data.

We present an extension to Jaynes’ maximum entropy principle that incorporates latent variables. The principle oflatent maximum entropywe propose is different from both Jaynes’ maximum entropy principle and maximum likelihood estimation, but can yield better estimates in the presence of hidden variables and limited training data. We first show that solving for a latent maximum entropy model poses a hard nonlinear constrained optimization problem in general. However, we then show that feasible solutions to this problem can be obtained efficiently for the special case of log-linear models---which forms the basis for an efficient approximation to the latent maximum entropy principle. We derive an algorithm that combines expectation-maximization with iterative scaling to produce feasible log-linear solutions. This algorithm can be interpreted as an alternating minimization algorithm in the information divergence, and reveals an intimate connection between the latent maximum entropy and maximum likelihood principles. To select a final model, we generate a series of feasible candidates, calculate the entropy of each, and choose the model that attains the highest entropy. Our experimental results show that estimation based on the latent maximum entropy principle generally gives better results than maximum likelihood when estimating latent variable models on small observed data samples.

Simulation and analysis of red-crowned crane habitat suitability using maximum entropy and information entropy models

Entropy methods enable a convenient general approach to providing a probability distribution with partial information. The minimum cross-entropy principle selects the distribution that minimizes the Kullback–Leibler divergence subject to the given constraints. This general principle encompasses a wide variety of distributions, and generalizes other methods that have been proposed independently. There remains, however, some confusion about the breadth of entropy methods in the literature. In particular, the asymmetry of the Kullback–Leibler divergence provides two important special cases when the target distribution is uniform: the maximum entropy method and the maximum log-probability method. This paper compares the performance of both methods under a variety of conditions. We also examine a generalized maximum log-probability method as a further demonstration of the generality of the entropy approach.

The modeling of complex service systems entails capturing many sub-components of the system, and the dependencies that exist among them in the form of a joint probability distribution. Two common methods for constructing joint probability distributions from experts using partial information include maximum entropy methods and copula methods. In this paper we explore the performance of these methods in capturing the dependence between random variables using correlation coefficients and lower-order pairwise assessments. We focus on the case of discrete random variables, and compare the performance of these methods using a Monte Carlo simulation when the variables exhibit both independence and non-linear dependence structures. We show that the maximum entropy method with correlation coefficients and the Gaussian copula method perform similarly, while the maximum entropy method with pairwise assessments performs better particularly when the variables exhibit non-linear dependence.

This article introduces two new maximum entropy (ME) methods for modeling the distribution of time to an event. One method is within the classical ME framework and provides characterizations of change point models such as the piecewise exponential distribution. The second method uses the entropy of the equilibrium distribution (ED) for the objective function and provides new characterizations of the exponential, Weibull, Pareto, and uniform distributions. With the same moment constraints, the classical ME and the maximum ED entropy algorithms generate different models for the interarrival time. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 427–434, 2014

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author's institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The decision tree model, with an F1 score of 0.458 and accuracy of 0.862, outperforms both the naive Bayes (F1 score of 0.381) and maximum entropy (F1 score of 0.387) models. The models were analyzed to determine predictive features, with term frequency of n-grams such as "renal neoplasm" and "evalu with enhanc" being most predictive of a follow-up recommendation. Key to maximizing performance was feature engineering that extracts predictive information and appropriate selection of machine learning algorithms based on the feature set.

Climate change impacts on tropical forests and tree species have been documented as changes in distribution, growing period, phenology, habitat, productivity, species composition, and migration. This study attempts to assess the current and future habitat suitability distribution of two dominant species of Central India, teak (Tectona grandis Linn. f.) and sal (Shorea robusta Gaertn. f.) using a maximum entropy (MaxEnt) model to predict species occurrences by finding the distribution that has the most spread. The future suitable habitat ranges of the species were modeled for two time periods (2050 and 2070) and two representative concentration pathways (RCP 2.6 and 8.5). Representative concentration pathways are scenarios that describe alternative trajectories for carbon dioxide emissions and the resulting atmospheric concentration from 2000 to 2100. We collected and modeled the spatially well-dispersed species occurrence points of teak and sal. The results suggested an increase in suitable habitat range for teak and a decrease for sal for both time periods and RCPs. Furthermore, the jackknife analysis identified temperature and precipitation seasonality as the major variables that influence the distribution of teak. In the case of sal, minimum temperature makes the maximum contribution to distribution changes. The suitable silvicultural strategies for forest management are proposed based on the future distribution of species in relation to the climate variables responsible for the change in their distribution range. These findings and strategies will help forest departments build future management plans for teak and sal forest with a focus on minimizing the impact of climate change. Study Implications: Our study used maximum entropy (MaxEnt) modeling to understand the impact of changing climate on the distribution of teak and sal forests of central India and to propose future silvicultural strategies. The study used MaxEnt model for two time periods and two climate change scenarios at highest resolution. An increase in future suitable habitat for teak and a decrease for sal is predicted by the model. Temperature for teak and precipitation for sal were identified as the major influencing climatic variables. We recommend forest and other related government departments to commission focused research to understand the changing patterns of species with climate change and design appropriate silviculture strategies for effective management.

Intrusion detection is a critical component of secure information systems. Current intrusion detection systems (IDS) especially NIDS (Network Intrusion Detection System) examine all data features to detect intrusions. However, some of the features may be redundant or contribute little to the detection process and therefore they have great impact on the system performance. This paper proposes a lightweight intrusion detection model that is computationally efficient and effective based on feature selection and Maximum Entropy (ME) model. Firstly, the issue of identifying important input features is addressed. Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, therefore results to faster and more accurate detection. Secondly, classic ME model is used to learn and detect intrusions using the selected important features. Experimental results on the well-known KDD 1999 dataset show the proposed model is effective and can be applied to real-time intrusion detection environments.

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变化情景下基于最大熵模型的青海云杉潜在分布格局模拟 DOI: 10.5846/stxb201809151999 作者: 作者单位: 甘肃农业大学林学院,兰州大学生命科学学院,甘肃农业大学林学院,甘肃农业大学林学院 作者简介: 通讯作者: 中图分类号: 基金项目: 甘肃农业大学科技创新基金-公招博士科研启动基金项目（GSAU-RCZX01707）；学科建设专项基金项目（GAU-XKJS-2018-099）；国家自然基金项目（31860197） Simulation of the potential distribution patterns of Picea crassifolia in climate change scenarios based on the maximum entropy (Maxent) model Author: Affiliation: Gansu Agricultural University, Lanzhou 730070 Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:青海云杉（Picea crassifolia）是我国青藏高原东北缘特有树种，在维系我国西北地区生态平衡、水土保持、水源涵养和生物多样性等方面发挥着重要作用。基于其分布范围内的69个地理分布样点，利用最大熵（Maxent）模型对现实气候条件下青海云杉的潜在分布及其分布的主导气候因子进行分析，同时结合3种大气环流模型模拟青海云杉在3种气候变化情景（温室气候排放量不同）下未来2050s和2080s潜在分布区的变化。结果表明：Maxent模型对青海云杉潜在分布区的预测具有极高的准确度，所有模型的平均受试者工作特征曲线下面积（AUC测试值）均高于0.99；Jackknife检验和气候因子响应曲线表明年最低降雨量是限制青海云杉分布的主导因子；当前青海云杉的潜在分布区主要集中于青海东部、甘肃东南部、宁夏大部分地区、西藏东部、四川西部山区以及陕西、新疆和内蒙古部分地区。在未来3种增温情景下，青海云杉在2050s和2080s的潜在分布总面积与当前相比变化不明显，但不同适生等级的潜在分布面积变化较大，其中，中度适生区和低度适生区受气候增温影响显著，中度增温下这些区域在2080s的面积明显增大，而高度适生区（核心分布）则在所有增温情景下均呈缩小趋势。同时，在未来3种增温情景下，青海云杉在2050s和2080s的潜在分布区有向北移动趋势，但其心分布区域（高度适生区）仍然以青海东部、甘肃北部为主，无明显变迁趋势。从气候因素角度考虑，本研究表明未来气候变化情景下，青海云杉依然在西部高山地区，特别是作为我国重要生态屏障的祁连山、贺兰山等山区具有重要的经济价值并将持续其生态服务功能。 Abstract:Qinghai spruce (Picea crassifolia) is the endemic tree species in the northeastern margin of the Qinghai-Tibet Plateau (QTP) in China. It plays an important role in maintaining ecological balance, biodiversity, and soil and water conservation in northwestern China. In this study, the current potential distribution of Qinghai spruce and its dominant climatic factors were analyzed firstly based on 69 distributional records via the maximum entropy model (Maxent). Then the distribution patterns of Qinghai spruce under three climate change scenarios (i.e., the lowest, a moderate, and the highest greenhouse gas emission scenarios; RCP2.6, RCP4.5 and RCP8.5) with three general atmospheric circulation models were predicted by Maxent for two future decades:the 2050s and 2080s. The results showed that the accuracy of the Maxent model was pretty high for modeling potential distribution regions of Qinghai spruce, and the average values of the area under the receiver operator characteristic curve (AUC) were higher than 0.99. The results from the Jackknife test and climate factor response curves revealed that the annual lowest precipitation is the key factor that limited the distribution of Qinghai spruce; the current potential distribution regions of Qinghai spruce are mainly concentrated in eastern Qinghai, southeastern Gansu, most of Ningxia, eastern Tibet, mountainous areas of western Sichuan, and parts of Shaanxi, Xinjiang, and Inner Mongolia. The total potential distribution area of Qinghai spruce in the 2050s and 2080s was changed little compared to the current distribution in all three scenarios, but its potential distribution area at different levels of fitness was changed a lot. Among them, the regions with fitness of less than 0.1 and in the range of 0.1-0.3 were significantly affected by the climate changes, and their area was largely increased in the medium emission scenario. In contrast, the regions with fitness in the range of 0.5-0.8 were shrunk in all scenarios. At the same time, the distribution patterns of Qinghai spruce in the 2050s and 2080s had a tendency to move northward in all scenarios, whereas its central distributional regions (more suitable regions) were still kept in eastern Qinghai and northern Gansu, with no obvious trend to change. From the perspective of climatic factors, this study showed that Qinghai spruce would continue to have important economic values and ecological service functions in the western mountains, especially in the Qilian Mountains and Helan Mountains, which are the important ecological barriers in China. 参考文献 相似文献 引证文献

The convergence rates of generalized iterative scaling (GIS) and improved iterative scaling (IIS) algorithms for fitting maximum entropy (ME) models are investigated and also a particular linear dynamic system monitoring with partial active measurements is studied. An information-theoretic based measurement scheme is derived to select informative hidden states, which is validated on a problem of origin-destination matrix estimation for Internet traffic.

To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Multivariate maximum entropy identification, transformation, and dependence

Two Chebyshev recursion methods are presented for calculations with very large sparse Hamiltonians, the kernel polynomial method (KPM) and the maximum entropy method (MEM). They are applicable to physical properties involving large numbers of eigenstates such as densities of states, spectral functions, thermodynamics, total energies for Monte Carlo simulations and forces for tight binding molecular dynamics. this paper emphasizes efficient algorithms.

Background and Aim:African swine fever (ASF) is an infectious disease and a major viral pig disease that threatens pork production in several locations globally. The mortality rate of ASF in domestic pigs is very high, causing a decrease in pig populations and significant economic losses for farmers. Environmental or ecological risk factors are the most important associated with the spread of the ASF virus. Environmental (or ecological) niche models are commonly used to estimate the probability of an event using the maximum entropy (Maxent) method. This study aimed to estimate the probability risk of future ASF outbreaks in North Sumatra, Indonesia.Materials and Methods:Secondary data from the National Animal Health System Database (iSIKHNAS), including data on the ASF outbreaks of 2019–2020 in North Sumatra, Indonesia, were used in this study. The first analysis performed involved the identification of environmental risk factors using multiple regression analysis. The second analysis performed was the estimation of probability risk for future ASF outbreaks in North Sumatra, Indonesia, using the Maxent method. Data processing was performed using Microsoft Excel, ArcGIS version 10.5 software (ESRI, California, United States), Maxent version 3.4.4 software, and Rstudio (http://www.r-project.org/).Results:The Maxent method was found to be highly accurate with a statistically significant area under the curve value of 0.860. The greatest contributing environmental factor identified by the model was the harbor, which contributed 57%. The range of high probability risk of future ASF outbreaks was found to be 0.723–0.84.Conclusion:The estimation of the highest probability risk of future ASF outbreaks in North Sumatra, Indonesia, was 0.723–0.84. The most contributing environmental factor identified using the Maxent method was harbors, at 57%. This methodology can be used to carry out subsequent ASF analyses and contribute to developing prevention and control strategies in this area.