Causal modelling combining instantaneous and lagged effects

Sensitivity to evidence in Gaussian Bayesian networks using mutual information

Extreme inaccuracies in Gaussian Bayesian networks

With an experimental model for SD (Sparague-Sawley) rat focal cerebral ischemia presented, the EEG (electroencephalogram) signals from ischemic and normal regions are extracted. A non-Gaussian AR model is used for the EEG signals and the order of AR model is determined by bispectral cross correlation (BCC). Then based on above AR model, the constrained third-order mean (CTOM) method is proposed to estimate the bispectrum of EEG signals. Results show that the ischemic and normal regions can be clearly distinguished by the bispectrum of EEG signals. Both the extent and location of focal cerebral ischemic can be detected by the proposed method.

Gaussian Bayesian network model of healthcare, food and energy sectors in the pandemic: Türkiye case

A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.

Multi-rate Gaussian Bayesian network soft sensor development with noisy input and missing data

In the previous chapters, we discussed autoregressive linear mixed effects models. In this section, we discuss the relationships between the autoregressive linear mixed effects models and nonlinear mixed effects models, growth curves, and differential equations. The autoregressive model shows a profile approaching an asymptote, where the change is proportional to the distance remaining to the asymptote. Autoregressive models in discrete time correspond to monomolecular curves in continuous time. Autoregressive linear mixed effects models correspond to monomolecular curves with random effects in the baseline and asymptote, and special error terms. The autoregressive coefficient is a nonlinear parameter, but all random effects parameters in the model are linear. Therefore, autoregressive linear mixed effects models are nonlinear mixed effects models without nonlinear random effects and have a closed form of likelihood. When there are time-dependent covariates, autoregressive linear mixed effects models are represented by a differential equation and random effects. The monomolecular curve is one of the popular growth curves. We introduce other growth curves, such as the logistic curves and von Bertalanffy curves, and generalizations of growth curves. Re-parameterization is often performed in nonlinear models, and various representations of re-parameterization in monomolecular and other curves are provided herein.

The fields of time series and graphical models emerged and advanced separately. Previous work on the structure learning of continuous and real-valued time series utilizes the time domain, with a focus on either structural autoregressive models or linear (non-)Gaussian Bayesian Networks. In contrast, a novel frequency domain approach is proposed to identify a topological ordering and learn the structure of multivariate time series. In particular, a class of complex-valued Structural Causal Models (cSCM) is defined at each frequency of the Fourier transform of the time series. Assuming that the time series is generated from the transfer function model, it is demonstrated that the topological ordering and the corresponding summary directed acyclic graph can be uniquely identified from cSCM. The performance of the algorithm is investigated using simulation experiments and real datasets.

Adding data process feedback to the nonlinear autoregressive model

Ivermectin is an anthelmintic drug that works by activating glutamate-gated chloride channel receptors (GluClRs) in nematode parasites. GluClRs belong to the Cys-loop receptor family that also includes glycine receptor (GlyR) chloride channels. GluClRs and A288G mutant GlyRs are both activated by low nanomolar ivermectin concentrations. The crystal structure of the Caenorhabditis elegans α GluClR complexed with ivermectin has recently been published. Here, we probed ivermectin sensitivity determinants on the α1 GlyR using site-directed mutagenesis and electrophysiology. Based on a mutagenesis screen of transmembrane residues, we identified Ala288 and Pro230 as crucial sensitivity determinants. A comparison of the actions of selamectin and ivermectin suggested the benzofuran C05-OH was required for high efficacy. When taken together with docking simulations, these results supported a GlyR ivermectin binding orientation similar to that seen in the GluClR crystal structure. However, whereas the crystal structure shows that ivermectin interacts with the α GluClR via H-bonds with Leu218, Ser260, and Thr285 (α GluClR numbering), our data indicate that H-bonds with residues homologous to Ser260 and Thr285 are not important for high ivermectin sensitivity or direct agonist efficacy in A288G α1 GlyRs or three other GluClRs. Our data also suggest that van der Waals interactions between the ivermectin disaccharide and GlyR M2-M3 loop residues are unimportant for high ivermectin sensitivity. Thus, although our results corroborate the ivermectin binding orientation as revealed by the crystal structure, they demonstrate that some of the binding interactions revealed by this structure do not pertain to other highly ivermectin-sensitive Cys-loop receptors.

Learning parameters of Bayesian networks from datasets with systematically missing data: A meta–analytic approach

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g‐index, q2‐index, and hr‐index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area.

For the past decades, Virtual Metrology (VM) has been widely studied and covered in the literature for semiconductor industries where cycle time is a critical aspect and the elimination of non-productive metrology measurements is expected to significantly contribute to its reduction. A wide variety of approaches has been proposed but not effectively implemented. An ideal VM model should be able to provide accurate predictions and to reveal the hidden relationship among production/process factors. For this aim, we employ the Gaussian Bayesian Network (GBN) to investigate the implicit relationship not only between the metrology and the control factors but also among the production/process parameters. Instead of working purely as a black-box data-driven methodology, GBN enables the flexibility to integrate domain knowledge through the corresponding connected graph. The effectiveness of proposed approach is validated using real industrial data from a Chemical-Mechanical Polishing (CMP) process.

Identifying the causalities for air pollutants and answering questions, such as, where do Beijing's air pollutants come from, are crucial to inform government decision-making. In this paper, we identify the spatio-temporal (ST) causalities among air pollutants at different locations by mining the urban big data. This is challenging for two reasons: 1) since air pollutants can be generated locally or dispersed from the neighborhood, we need to discover the causes in the ST space from many candidate locations with time efficiency; 2) the cause-and-effect relations between air pollutants are further affected by confounding variables like meteorology. To tackle these problems, we propose a coupled Gaussian Bayesian model with two components: 1) a Gaussian Bayesian Network (GBN) to represent the cause-and-effect relations among air pollutants, with an entropy-based algorithm to efficiently locate the causes in the ST space; 2) a coupled model that combines cause-and-effect relations with meteorology to better learn the parameters while eliminating the impact of confounding. The proposed model is verified using air quality and meteorological data from 52 cities over the period Jun 1st 2013 to May 1st 2015. Results show superiority of our model beyond baseline causality learning methods, in both time efficiency and prediction accuracy.

This paper presents a new blind direction of arrival (DOA) estimation approach for closely-spaced sources. The new method first estimates the autoregressive (AR) coefficients via an initial DOA estimation and then uses the AR coefficients for the linear extrapolation of the correlation matrix to implement a fine DOA estimation. Both initial and fine DOA estimations are performed using the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Unlike a conventional AR coefficient estimation method which estimates the AR coefficients on the snapshot basis, our AR coefficients are estimated in the correlation domain once for a block of snapshots, thus significantly reducing the computational complexity of the antenna array. Moreover, the proposed spatial extrapolation-based DOA estimation approach is analyzed using perturbation theory. Both the theoretical analysis and computer simulations show that the proposed method outperforms the conventional techniques in terms of the mean square error (MSE) of the DOA estimation when the angle of separation of the signal sources is very small.