Dependence Of Probability Research Articles

Алгоритмы и программные средства адаптации базы знаний информационных систем управления проектами

The effectiveness of design solutions largely depends on the promptness of processing a large amount of data from various sources, which determines the feasibility of using information decision support systems (IDSS) in the field of project management. The peculiarities of information processes in project management greatly complicate or even make it impossible to implement in practice methods for constructing analytical, as well as probabilistic and statistical dependencies between the characteristics of the modeled project management system and the indicators of its internal and external environment. In this regard, as an algorithmic support for IDSS for project management, it is promising to use precedent methods for analyzing information based on knowledge about similar situations previously observed in the practice of project management, and representing knowledge in the form of ontologies. Analysis of practical situations in the field of project management makes it possible to substantiate the expediency of organizing a monitoring procedure for the IDSS knowledge base, based on the results of which decisions on its adaptation are made. The article proposes the main ways of this adaptation: changing the structure and basic elements (first of all, concepts) of ontologies; clarification of the structure of the description of current situations and, therefore, precedents. The developed algorithm for monitoring the IDSS knowledge base on project management for the analysis and identification of typical situations of the feasibility of changing it is described. The algorithm is distinguished by the possibility of developing recommendations on the modification of ontologies based on a fuzzy classification of search results and using precedents relevant to current situations. A procedure is proposed for changing the structure of the description of precedents, taking into account the results of assessing the indices of the fuzzy correspondence of the characteristics of the existing precedents to the characteristics of the project being implemented. A description of a computer program that implements the proposed algorithm and its components, as well as the results of its application are given.

Causal contextuality and contextuality-by-default are different concepts

In the framework of operational theories and ontological models, contextuality is defined as a causal (and hence probabilistic) dependence of the distribution of measurement outcomes in a given ontic state on simultaneous measurements. I will refer to this concept as causal contextuality and compare it with contextuality-by-default (CbD-contextuality) defined by Dzhafarov, Kujala and colleagues. The two concepts are different. First, causal contextuality is a property of the ontological model while CbD-contextuality is a feature of the operational theory. Second, signaling operational theories can be CbD-noncontextual but they admit only causally contextual ontological models. To highlight the difference between the two concepts, I construct a simple toy model which is causally contextual but CbD-noncontextual.

Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

Student Modelling on Language Teaching Based on Bayesian Networks

Acquiring a reliable student model is the principal task of an Intelligent Tutoring System (ITS). A precisely defined student model is also the key term for the success of ITSs. In this paper, a study on inferring the accurate student model on language learning is offered by utilizing artificial intelligence. In addition to the general structure of an ITS, a probabilistic model for inference using Bayesian Networks is stated in the paper. Bayesian Networks are acyclic directed graphs in which nodes represent random variables and arcs represent direct probabilistic dependences among them. In this study, graphical models and structures are implemented in a general-purpose decision modelling system SMILE and its Windows user interface, GeNIe, developed at the Decision Systems Laboratory. GeNIe user interface is also used in this study to perform Influence diagrams. Influence diagrams represent decision problems and help to choose a decision alternative with the highest expected gain. Toward the end of the study, an ITS student model which is directly associated with a standard proficiency level is aimed to be developed. This model is also complemented with a domain model which incorporates language components such as grammar, vocabulary, and functions of language in different cases. At the end of the study, an evaluation of the model is performed with an experimental study and the results show that the participants worked with offered ITS model gathered close results when compared to those obtained by the participants who worked with a real tutor.

Quantification of multiple-variate random field by synthesizing the spatial correlation function of prime variable and copula function

The probabilistic dependence in a multi-variate random field consists of two parts: the spatial dependence of a random quantity at different positions and the probabilistic dependence between different random variables at the same position. The classical model cannot capture the possible non-Gaussian dependence or nonlinear dependence between different random variables. To this end, in this paper, an approach by synthesizing the spatial correlation function and the multi-variate copula function (SCFVCF) is proposed. In this model, the correlation function model is adopted to quantify the spatial dependence involved in the random field of the prime variable, and the copula function model is adopted to quantify the dependence configuration between subordinate variables. The properties of such multi-variate random fields are then studied. To generate samples of such multi-variate random fields, the spectral representation method is incorporated with the conditional sampling method. As an example, to illustrate the application of SCFVCF, the random field of the constitutive parameters of concrete is adopted. The results demonstrate that the proposed method can capture the spatial dependence of compressive strength, and at the same time the dependence configuration between different parameters is consistent with the test complete compressive stress-strain curves.

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming.

Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create suboptimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This article presents Grammar-Based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.

Random transition of failure modes in concrete shear walls with constitutive parameters involving spatial variation

There are two types of probabilistic dependence between the damage constitutive parameters of concrete, i.e., the probabilistic dependence between the parameters at the same position and the spatial dependence of a parameter at different positions. In this paper, a newly proposed multi-variate random field model by synthesizing the spatial correlation function with the multivariate copula function (SCFVCF) is adopted to generate spatially variant constitutive relationship of concrete in concrete shear walls. Then the failure modes and hysteresis performance of an I-type reinforced concrete shear wall with spatially variant concrete constitutive relationship under compressive and cycling horizontal loading are studied. The failure modes of the shear wall are random in nature due to the spatial randomness involved in the constitutive relationship, e.g., the damage position of the shear wall may transfer to the weaker area at the upper elements for some samples. By observation, the failure modes can be classified into four types. Particularly significant is that random transition of failure modes will occur when the spatial correlation length increases, and consequently the probability of occurrence of different failure modes changes against the spatial correlation length. This means that if the two types of dependence between the material constitutive parameters are not reasonably taken into account, the results of failure analysis of reinforced concrete shear walls might be misleading.

Probabilistic Dependency Graphs

We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.

Information Structures for Causally Explainable Decisions.

For an AI agent to make trustworthy decision recommendations under uncertainty on behalf of human principals, it should be able to explain why its recommended decisions make preferred outcomes more likely and what risks they entail. Such rationales use causal models to link potential courses of action to resulting outcome probabilities. They reflect an understanding of possible actions, preferred outcomes, the effects of action on outcome probabilities, and acceptable risks and trade-offs—the standard ingredients of normative theories of decision-making under uncertainty, such as expected utility theory. Competent AI advisory systems should also notice changes that might affect a user’s plans and goals. In response, they should apply both learned patterns for quick response (analogous to fast, intuitive “System 1” decision-making in human psychology) and also slower causal inference and simulation, decision optimization, and planning algorithms (analogous to deliberative “System 2” decision-making in human psychology) to decide how best to respond to changing conditions. Concepts of conditional independence, conditional probability tables (CPTs) or models, causality, heuristic search for optimal plans, uncertainty reduction, and value of information (VoI) provide a rich, principled framework for recognizing and responding to relevant changes and features of decision problems via both learned and calculated responses. This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to support responses on multiple time scales, and evaluate and update causal models and plans in light of new data. The resulting causally explainable decisions make efficient use of available information to achieve goals in uncertain environments.

Observation-based approximate dependency modeling and its use for program slicing

While dependency analysis is foundational to much program analysis, many techniques have limited scalability and handle only monolingual systems. We present a novel dependency analysis technique that aims to approximate program dependency from a relatively small number of perturbed executions. Our technique, MOAD (Modeling Observation-based Approximate Dependency), reformulates program dependency as the likelihood that one program element is dependent on another (instead of a Boolean relationship). MOAD generates program variants by deleting parts of the source code and executing them while observing the impact. MOAD thus infers a model of program dependency that captures the relationship between the modification and observation points. We evaluate MOAD using program slices obtained from the resulting probabilistic dependency models. Compared to the existing observation-based backward slicing technique, ORBS, MOAD requires only 18.6% of the observations, while the resulting slices are only 12% larger on average. Furthermore, we introduce the notion of the observation-based forward slices. Unlike ORBS, which inherently computes backward slices, MOAD’s model’s dependences can be traversed in either direction allowing us to easily compute forward slices. In comparison to the static forward slice, MOAD only misses deleting 0–6 lines (median 0), while excessively deleting 0–37 lines (median 8) from the slice.

Entropical analysis of an opinion formation model presenting a spontaneous third position emergence

Characterization of complexity within the sociological interpretation has resulted in a large number of notions, which are relevant in different situations. From the statistical mechanics point of view, these notions resemble entropy. In a recent work, intriguing non-monotonous properties were observed in an opinion dynamics Sznajd model. These properties were found to be consequences of the hierarchical organization assumed for the system, though their nature remained unexplained. In the present work we bring an unified entropical framework that provides a deeper understanding of those system features. By perfoming numerical simulations, the system track probabilistic dependence on the initial structures is quantified in terms of entropy. Several entropical regimes are unveiled. The myriad of possible system outputs is enhanced within a maximum impredictability regime. A mutual structural weakness of the initial parties could be associated to this regime, fostering the emergence of a third position.

Jump point identification in hydro-meteorological time series by crossing methodology

The climate change impact appears as a decreasing or increasing monotonic trend in hydro-meteorology time series records due to greenhouse gas (GHG) emissions causing to global warming and climate change impacts. On the other hand, there may be abrupt changes in the form of jumps in these series due to natural and engineering activities. Although trend identification methods are rather common in the literature, jump determination conventional methodologies are rather rare and their applications present some restrictive asumptions like the serial independence and normal (Gaussian) probability distribution function (PDF). The methodology presented in this paper is away from each of such assumptions and it depicts the minimum number of upcrossing along horizontal truncation levels within the time series variation domain. The applications of the methodology are given for annual Danube River discharge records, Romania; New Jersey rainfall and temperature records, USA; monthly rainfall records and Van Lake level fluctuations, Turkey.

V-SVR+: Support Vector Regression With Variational Privileged Information

Many regression tasks encounter an asymmetric distribution of information between training and testing phases where the additional information available in training, the so-called privileged information (PI), is often inaccessible in testing. In practice, the privileged information in training data might be expressed in different formats, such as continuous, ordinal, or binary values. However, most the existing learning using privileged information (LUPI) paradigms primarily deal with the continuous form of PI, preventing them from managing variational PI, which motivates this research. Therefore, in this paper, we propose a unified framework to systematically address the aforementioned three forms of privileged information. The proposed V-SVR+ method integrates continuous, ordinal, and binary PI into the learning process of support vector regression (SVR) via three losses. For continuous privileged information, we define a linear correcting (slack) function in the privileged information space to estimate slack variables in the standard SVR method using privileged information. For the ordinal relations of privileged information, we first rank the privileged information and then, regard this ordinal privileged information as auxiliary information used in the learning process of the SVR model. For the binary or Boolean privileged information, we infer a probabilistic dependency between the privileged information and labels from the summarized privileged information knowledge. Then, we transfer the privileged information knowledge to constraints and form a constrained optimization problem. We evaluate the proposed method in three applications: music emotion recognition from songs with the help of implicit information about music elements judged by composers; multiple object recognition from images with the help of implicit information about the object’s importance conveyed by the list of manually annotated image tags; and photo aesthetic assessment enhanced by high-level aesthetic attributes hidden in photos. Experiment results demonstrate that the proposed methods are superior to the classic learning paradigm when solving practical problems.

You say you want a revolution: two notions of probabilistic independence

Branden Fitelson and Alan Hajek have suggested that it is finally time for a “revolution” in which we jettison Kolmogorov’s axiomatization of probability, and move to an alternative like Popper’s. According to these authors, not only did Kolmogorov fail to give an adequate analysis of conditional probability, he also failed to give an adequate account of another central notion in probability theory: probabilistic independence. This paper defends Kolmogorov, with a focus on this independence charge. I show that Kolmogorov’s sophisticated theory of conditional distributions, advocated by authors such as Kenny Easwaran but often overlooked by philosophers, has the resources to define the notion of independence Fitelson and Hajek seek. I then argue that Kolmogorov’s original definition of independence, once suitably reinterpreted as capturing a slight logical weakening of this notion, is not so problematic after all. Indeed, rather than discarding Kolmogorov’s definition, we should make room for two distinct notions of probabilistic independence, one stronger and one weaker, which play different theoretical roles.

Explicit polynomial regression models of wind characteristics and structural effects on a long‐span bridge utilizing onsite monitoring data

From 2010 to 2016, wind speed time history and synchronous acceleration responses along Xihoumen Bridge (XB) girder were recorded by a health-monitoring system. Based on this dataset, wind speed, wind direction, angle of attack (AoA), turbulence intensity, and turbulence length scale are studied based on 10-min time intervals. The environmental wind characteristics and synchronous structural effects are decoupled by the control variable method. The probabilistic dependence between wind parameters and structural responses is explored by explicit polynomial regression equations. With wind environment parameters as input, the acceleration responses of the bridge could be rapidly predicted with a certain confidence interval using decoupling and regression modeling (DRM). The proposed DRM can reproduce the buffeting acceleration responses when the bridge is subject to future potential strong typhoon attacks. The key factors affecting the buffeting responses of XB are analyzed and discussed. Generally, the buffeting acceleration responses of XB are significantly affected by wind speed and turbulence intensity rather than by turbulence length scale, which agrees well with previous observations.

A network approach to parental burnout

BackgroundThe use of network analyses in psychology has increasingly gained traction in the last few years. A network perspective views psychological constructs as dynamic systems of interacting elements. ObjectiveWe present the first study to apply network analyses to examine how the hallmark features of parental burnout — i.e., exhaustion related to the parental role, emotional distancing from children, and a sense of ineffectiveness in the parental role — interact with one another and with maladaptive behaviors related to the partner and the child(ren), when these variables are conceptualized as a network system. Participants and settingIn a preregistered fashion, we reanalyzed the data from a French-speaking sample (n = 1551; previously published in Mikolajczak, Brianda et al., 2018), focusing on seven specific variables: the three hallmark parental burnout features, partner conflict, partner estrangement, neglectful behavior toward children, and violent behavior toward children. MethodsWe computed two types of network models, a graphical Gaussian model to examine network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the different variables. ResultsBoth network models pointed to emotional distance as an especially potent mechanism in activating all other nodes. ConclusionsThese results suggest emotional distance as critical to the maintenance of the parental burnout network and a prime candidate for future interventions, while affirming that network analysis can successfully expose the structure and relationship of variables related to parental burnout and its consequences related to the partner and the child(ren).

Age-dependent statistical learning trajectories reveal differences in information weighting.

Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. The effect of aging on SL is still unclear. Here, we explore statistical learning in healthy adults (40 younger and 40 older). The novel paradigm tracks learning trajectories and shows age-related differences in overall performance, yet similarities in learning rates. Bayesian models reveal further differences between younger and older adults in dealing with uncertainty in this probabilistic SL task. We test computational models of 3 different learning strategies: (a) Win-Stay, Lose-Shift, (b) Delta Rule Learning, (c) Information Weights to explore whether they capture age-related differences in performance and learning in the present task. A likely candidate mechanism emerges in the form of age-dependent differences in information weights, in which young adults more readily change their behavior, but also show disproportionally strong reactions toward erroneous predictions. With lower but more balanced information weights, older adults show slower behavioral adaptation but eventually arrive at more stable and accurate representations of the underlying transitional probability matrix. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A statistical theory of the strength of epidemics: an application to the Italian COVID-19 case.

The proposed theory defines a relative index of epidemic lethality that compares any two configurations in different observation periods, preferably one in the acute and the other in a mild epidemic phase. Raw mortality data represent the input, with no need to recognize the cause of death. Data are categorized according to the victims’ age, which must be renormalized because older people have a greater probability of developing a level of physical decay (human damage), favouring critical pathologies and co-morbidities. The probabilistic dependence of human damage on renormalized age is related to a death criterion considering a virus spread by contagion and our capacity to cure the disease. Remarkably, this is reminiscent of the Weibull theory of the strength of brittle structures containing a population of crack-like defects, in the correlation between the statistical distribution of cracks and the risk of fracture at a prescribed stress level. Age-of-death scaling laws are predicted in accordance with data collected in Italian regions and provinces during the first wave of COVID-19, taken as representative examples to validate the theory. For the prevention of spread and the management of the epidemic, the various parameters of the theory shall be informed on other existing epidemiological models.

Hierarchical Independence Thresholding for learning Bayesian network classifiers

Bayesian networks are powerful tools for knowledge representation and inference under conditions of uncertainty. However, learning an optimal Bayesian network classifier (BNC) is an NP-hard problem since its topology complexity increases exponentially with the number of attributes. Researchers proposed to apply information-theoretic criteria to measure conditional dependence, and independence assumptions are introduced implicitly or explicitly to simplify the network topology of BNC. In this paper, we clarify the mapping relationship between conditional mutual information and local topology, and then illustrate that informational independence does not correspond to probabilistic independence, the criterion of probabilistic independence does not necessarily hold for the independence topology. A novel framework of semi-naive Bayesian operation, called Hierarchical Independence Thresholding (HIT), is presented to efficiently identify informational conditional independence and probabilistic conditional independence by applying an adaptive thresholding method, redundant edges will be filtered out and the learned topology will fit the data better. Extensive experimental evaluation on 58 publicly available datasets reveals that when HIT is applied to BNCs (such as tree augmented Naive Bayes or k-dependence Bayesian classifier), the final BNCs achieve competitive classification performance compared to state-of-the-art learners such as Random Forest and Logistic regression.

Leave-One-Out Approach for Matrix Completion: Primal and Dual Analysis

In this paper, we introduce a powerful technique based on Leave-One-Out analysis to the study of low-rank matrix completion problems. Using this technique, we develop a general approach for obtaining fine-grained, entrywise bounds for iterative stochastic procedures in the presence of probabilistic dependency. We demonstrate the power of this approach in analyzing two of the most important algorithms for matrix completion: (i) the non-convex approach based on Projected Gradient Descent (PGD) for a rank-constrained formulation, also known as the Singular Value Projection algorithm, and (ii) the convex relaxation approach based on nuclear norm minimization (NNM). Using this approach, we establish the first convergence guarantee for the original form of PGD without regularization or sample splitting , and in particular shows that it converges linearly in the infinity norm . For NNM, we use this approach to study a fictitious iterative procedure that arises in the dual analysis . Our results show that NNM recovers an $d$ -by- $d$ rank- $r$ matrix with $\mathcal {O}(\mu r \log (\mu r) d\log d)$ observed entries. This bound has optimal dependence on the matrix dimension and is independent of the condition number. To the best of our knowledge, none of previous sample complexity results for tractable matrix completion algorithms satisfies these two properties simultaneously.