基于真假设比例广义推断的多重检验方法

研究了多重假设检验方法的改进问题,基于真假设所占比例的广义推断,利用其广义置信上限对现有的多重假设检验方法进行改进.该方法更好地利用了水平,在功效上有很大提升.采用广义推断方法,即使在假设个数比较小时,也能对真假设所占比例做出较好推断.模拟结果表明,该方法显著提高了传统方法的功效.     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These ‘multi-exits’ models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

基因调控网络推断研究进展

随着高通量技术的发展,越来越多的生物医学组学数据亟需处理与分析,基于运筹优化的生物信息学方法是有效解析高维生物医学数据的重要途径之一。综述了近年来在基因调控网络推断方面的研究进展。针对不同类型的转录组学数据和研究目的,分别建立了相应的基因调控网络推断方法,主要包括先验基因调控网络数据库的建立、基于条件互信息的因果网络推断、基于微分方程的动态基因调控网络推断、转录调控和转录后调控协同作用的网络推断以及基因调控网络活性评价等,并展望了基因调控网络推断的重要研究方向。     

一个捕捉与再捕捉模型的贝叶斯分析

本文在无信息先验和Jeffreys先验下 ,就捕捉与再捕捉试验和多次重复的捕捉与再捕捉试验两种情况 ,推导了封闭总体中个体总数N的贝叶斯点估计与区间估计 ,并计算了一个实例     

时间序列多个突变点的贝叶斯推断——对我国GDP序列的实证分析

突变点的存在对经济分析与建模会产生重要影响."邹检验"仅仅在序列存在一个突变点时有效.为了对序列中可能存在的多个突变点进行判断,引入了基于贝叶斯推断的多个突变点判断理论,并将该理论应用于我国GDP序列中.笔者检测出该序列存在三个突变点,分别位于1961年,1976年,1989年.此外,发现加入合理的突变点后,模型的预测精度得到显著的提高.     

Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects

Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas previous work either relies on a less flexible parameterization of the model, or requires a large amount of data, our formulation allows for both a flexible model and learning when data are scarce. We continue the line of work of Bayesian inference for Hawkes processes, and derive an inference algorithm by performing inference on an aggregated sum of Gaussian Processes. Approximate Bayesian inference is achieved via data augmentation, and we describe a mean-field variational inference approach to learn the model parameters. To demonstrate the flexibility of the model we apply our methodology on data from different domains and compare it to previously reported results.     

基于神经网络和模糊逻辑的加工误差源诊断系统

文章介绍了采用多层诊断方法和加工质量智能分析和诊断系统。此系统采用神经网络、基于规则的ＡＣＦＥ集和模糊推理来诊断误差源；系统建立了产品模型、加工过程模型、加工环境模型和检测模型；并采用面向对象的方法来组织信息；最后系统用箱体类零件在Ｔ１０加工中心加工进行了验证。     

模糊推理在企业经营决策中的应用

运用模糊推理理论建立决策模型，解决了企业的成本－利润双因素模糊决策问题．根据企业实际生产经营状况，确定出最大利润额下的产品成本状态．