Chance Variables Research Articles

Analisis Zat Gizi Mikro dan Tinggi Badan Ibu Balita terhadap Kejadian Stunting di Daerah Gambut dan Aliran Sungai

The issue of hindering can be impacted by the insufficient and incessant quality and amount, both small scale and large scale, particularly from the hatchling to the age of 2 a long time, and wiped out children. The frequency of hindering can too be caused by hereditary variables (heredity) where the hereditary message gotten by children from guardians put away in Deoxyribose Nucleic Corrosive (DNA) will show the physical shape and potential of the child, even though this figure is the as it were a figure that decides the baby's development and advancement. This thinks about points to decide the micronutrient status and only the mother's stature with the hindering rate in peat and waterway bowl regions. This thinks about is an expository plan with a case-control plan between peat zones and waterway streams, with a review approach to hindering chance variables. The number of tests is 100 children beneath five with 50 cases and 50 controls. Factual examination to be carried out incorporates univariable, bivariable examination with Calculated Relapse. The comes about appeared that there was a critical relationship (p<0.05) between micronutrients and hindering in peat ranges and riverbanks. Little children in peat and riverbank zones who encounter micronutrient admissions have 0.24 times and 5.8 times the chance of encountering hindering. There was no critical relationship between maternal stature (TB) and the rate of hindering in both peat and watershed ranges (p>0.05). In any case, moms of little children with TB < 150 cm were cut 1.7 times in peat zones and 0.54 times in watersheds to grant birth to hindering little children. Conclusion. There's a noteworthy relationship between micronutrient status and no noteworthy relationship between maternal stature status and hindering in peat ranges and waterway flows.

Influencing Risk Factors in Construction Projects based on Time and Cost Overrun

The development business is considered as a standout amongst the essential ventures everywhere globally since it is the foundation of the economy of our nation. It recognized that a large portion of the development extends in India presented to cost and time overwhelm that isn't attractive. In the development ventures, there are such a large number of exercises to be tended to like asset activation, coordination, time the board, and material administration to finish the task on time inside the allotted expense. It comprehended that each action should be observed and ought not to permit slack anytime. Be that as it may, in the present situation, a lot of studies should be done to streamline the exercises to complete a gainful task. In the development business, it is essential to have control of the cost execution of activities to guarantee the development cost is inside the spending limit. Cost and Time are among the real thought all through the undertaking course of stages and can see as the most significant variables of a venture and the main impetus of task achievement. In this investigation, chance variables related to development ventures have recognized, and furthermore, the seriousness of the risk has additionally dissected, and the solid outcomes have examined. This investigation comprises of three stages: The main period of the venture is gathering sorts of writing universally and discover the effectively recognized risk factors in this specific southern region. The second stage incorporates the survey plan for the recognized risk components and circulation of polls to neighbourhood contractual workers, advisors, proprietors and every single other partner of the task. The third period of the proposal concentrated on information examination and result talk. SPSS programming had been utilized for examining the information procured from the surveys. From this examination, it was fathomed that the peril earnestness changed in low and high spending improvement adventures. The thinking which ought to be grasped for managing the risks perceived for low and high spending errands has been discussed and reasoned in this examination.

Chance involvement in goal scoring in football – an empirical approach

This study examines the impact of chance in football using the case of goal scoring. For this purpose, chance in goal scoring is defined as goals involving uncontrollable or not planned aspects. “Chance variables” were defined according to the principle of downward estimation, i.e. when one of these variables is involved this is an uncontrollable or not planned aspect of scoring a goal, although chance may also have an impact in instances other than those collected. Six “chance variables” were identified: deflection, rebound, goalkeeper involvement, post/bar hits, long distance shots, and defence involvement. In the 2011/2012 season, the 875 goals in German Bundesliga and the 1056 goals in English Premier League were examined for the presence of these variables. Of the goals examined, 47.0% showed at least one chance variable (deflection: 4.8%; rebound: 9.4%, goalkeeper: 2.5%; post/bar: 5.2%; distance: 13.1%, and defence: 22.5%). The two leagues differed significantly only in the sense of more goalkeeper interventions in the Bundesliga and more distance goals in the Premier League. The rate of chance-goals drops significantly with the number of prior goals in a match (χ2 = 17.59; p = 0.004). In both leagues, there is no significant correlation between final ranking and the percentage of chance-goals scored or conceded. If one accepts the selected chance variables as indicators of uncontrollable or not planned aspects of scoring, the results of this study can be taken as empirical proof for the existence and widespread prevalence of chance involvement in goal scoring in football with many interesting consequences.

An improved method for solving Hybrid Influence Diagrams

While decision trees are a popular formal and quantitative method for determining an optimal decision from a finite set of choices, for all but very simple problems they are computationally intractable. For this reason, Influence Diagrams (IDs) have been used as a more compact and efficient alternative. However, most algorithmic solutions assume that all chance variables are discrete, whereas in practice many are continuous. For such ‘Hybrid’ IDs (HIDs) the current-state-of-the-art algorithms suffer from various limitations on the kinds of inference that can be performed. This paper presents a novel method that overcomes a number of these limitations. The method solves a HID by transforming it to a Hybrid Bayesian Network (HBN) and carrying out inference on this HBN using Dynamic Discretization (DD). It generates a simplified decision tree from the propagated HBN to compute and present the optimal decisions under different decision scenarios. To provide satisfactory performance the method uses ‘inconsistent evidence’ to model functional and structural asymmetry. By using the entire marginal probability distribution of the continuous utility and chance nodes, rather than expected values alone, our method also enhances decision analysis by offering the possibility to consider additional statistics other than expected utility, such as measures of risk. We illustrate our method by using the oil wildcatter example and its variations with continuous nodes. We also use a financial score to combine risk and return measures, for illustration.

A Framework for Solving Hybrid Influence Diagrams Containing Deterministic Conditional Distributions

We describe a framework and an algorithm for approximately solving a class of hybrid influence diagrams (IDs) containing discrete and continuous chance variables, discrete and continuous decision variables, and deterministic conditional distributions for chance variables. A conditional distribution for a chance variable is said to be deterministic if its variances, for each state of its parents, are all zeroes. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. To mitigate the integration and optimization problems associated with solving hybrid IDs, we propose using mixture of polynomials approximations of conditional probability density and utility functions and piecewise linear approximations of nonlinear deterministic conditional distributions for continuous chance variables. The class of hybrid IDs that can be solved by our framework are those that do not involve divisions. The framework and algorithm are illustrated by solving two small examples of hybrid IDs.

Arc reversals in hybrid Bayesian networks with deterministic variables

This article discusses arc reversals in hybrid Bayesian networks with deterministic variables. Hybrid Bayesian networks contain a mix of discrete and continuous chance variables. In a Bayesian network representation, a continuous chance variable is said to be deterministic if its conditional distributions have zero variances. Arc reversals are used in making inferences in hybrid Bayesian networks and influence diagrams. We describe a framework consisting of potentials and some operations on potentials that allows us to describe arc reversals between all possible kinds of pairs of variables. We describe a new type of conditional distribution function, called partially deterministic, if some of the conditional distributions have zero variances and some have positive variances, and show how it can arise from arc reversals.

Graphical models for interactive POMDPs: representations and solutions

We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive influence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic influence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that I-POMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents’ candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance.

Decision making with hybrid influence diagrams using mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm.

Influence Diagrams with Continuous Decision Variables and Non-Gaussian Uncertainties

The continuous decision MTE influence diagram (CDMTEID) uses mixtures of truncated exponentials (MTE) potentials to approximate probability density functions (pdfs) and utility functions, and develops a piecewise-linear decision rule for continuous decision variables. The operations for solving CDMTEIDs are defined, and the abilities of this model to identify nonmonotonic decision rules and accommodate discrete variables with continuous parents are demonstrated. The CDMTEID solution to a problem with a continuous decision variable and a non-Gaussian continuous chance variable is presented and compared to a benchmark solution and existing models. The CDMTEID improves the quality of the decision rule and the value of information, as compared to other methods for the example problem.

Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation

The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.

Life Chances and Choices: Assessing Quality of Life among the Homeless

A Weberian lifestyles approach is employed to examine differences in quality of life among the homeless. Using a systematic random sample of 161 homeless people in a mid-sized, Southern metropolitan area, the study focuses on the impact of life chances and social choices on aspects of quality of life in this severely challenged population. Regression results show that a number of life chance and social choice variables affect general and domain-specific well-being. While chances and choices both contribute to these aspects of quality of life, there is only modest evidence of a mediating effect. With the exception of depressive symptoms, life chances appear to play a more important role in quality of life differences than life choices. The specific life chance and choice factors influencing aspects of quality of life vary with each separate well-being outcome. The implications of these findings for general sociology and homeless social policy are explored.

Software Review of Analytica 1.2

Analytica is an easy-to-learn, easy-to-use modeling tool that allows modelers to represent what they know through influence diagrams. These diagrams show which model quantities are derived from which others and indicate by shape and color the roles that different nodes play in the model, e.g., decision variables, chance variables, outcome variables, deterministic functions, or abstractions of sub-models. A wide variety of built-in probability distributions allow uncertainties about input values to be painlessly specified and propagated through the model via a fast, professional Monte-Carlo simulation engine. Resulting uncertainties and sensitivities about any quantity in the model can be viewed with admirable ease and flexibility by selecting among probability density, cumulative distribution, confidence band, sensitivity analysis, and other displays. Analytica features clever hierarchical model management and navigation features that serious model-builders will appreciate and that novice modelers will learn from as they are led to develop well-structured, well-documented models. Simple continuous (compartmental-flow) and Markov chain dynamic simulation models can be built by paying some detailed attention to arrays and indices, although Analytica does not support true discrete-event simulation. Within its chosen domain—uncertainty propagation through influence diagram models—Analytica is by far the easiest and best tool that we have seen.

Chicano Phenotype and Depression

Recent Chicano studies revealed that perceptions of discrimination and residential segregation increased whereas schooling and income decreased in accordance with a light-to-dark and European-to-Indian phenotype pattern. This study investigated the mental health implications of these results by examining the relationship of phenotype and the life chance variables of education, family income, and language proficiency to depression by nativity and gender Regression analyses were obtainedforfoursubgroups composed of Chicano men and women born in the United States and in Mexico. The prediction that the darker and more Indian phenotypes would have more depression was supported for U.S.-born males only. The pattern was unexpectedly in the opposite directionforMexican-born women, although in keeping with reports of severe stress due to role change among high-status Mexican women immigrants. Independent of phenotype, loss of Spanish language proficiency was also found to be a significant factor in how often some Chicanos felt depressed.

Discussion of the Paper by Hall and Johnstone

Discussion of the Paper by Hall and Johnstone

Entropy-based Optimal Group-testing Procedures

Two procedures for the group-testing problem based on the Shannon-entropy criteria are proposed. The model considered is that the N units are realizations of N Bernoulli independent and identically distributed (i.i.d.) chance variables with common, known probability q of an arbitrary unit being good and p =1 – q of it being defective. Both the algorithms introduced have low design complexity and yet provide near-optimal result. For N ≤ 5, one of the procedures introduced is optimal for selected values of q.

The statistical-inference approach to generalized thermodynamcis

Limit theorems, such as the central-limit theorem and the weak law of large numbers, are applicable to statistical thermodynamics for sufficiently large sample sizes of independent and identically distributed observations performed on extensive thermodynamic (chance) variables. The estimation of the intensive thermodynamic quantities is a problem in parametric statistical estimation. The normal approximation to the Gibbs’ distribution is justified by the analysis of large deviations. Statistical thermodynamics is generalized to include the statistical estimation of variance as well as mean values.

The Delphi method—a complement to research

AbstractIn making fire safety decisions the designer often finds that some input variables concerning his problems are chance variables. As detailed statistical data are rarely available the designer must rely largely on his expert judgment. With the application of the Delphi method such decisions can be optimized and formalized. The essence of the Delphi method is described and its use illustrated through an example related to the assessment of fire resistance requirements.

Coding of correlated sources with prescribed distortion by separated encoders

Let (X(n),Y(n)) = (X(1),Y(1)),...,(X(n),Y(n)) be n independent, identically distributed pairs of chance variables. Each of two separated encoders encodes a different sequence of (X(n),Y(n)) to within given respective distortions z(1),z(2). Call a region in the plane of the respective rates (R(1),R(2)) M-convex if it is convex, closed, and, to every point (r(1),r(2)), contains all points such that each of their coordinates is larger than or equal to the corresponding coordinate of (r(1),r(2)). This paper determines the (M-convex) region of achievable rates by giving its boundary. For z(1) = z(2) = 0, the region specializes to that of Slepian and Wolf. For z(2) = infinity and R(2) = H(Y(i)), the minimum R(1) is that of Wyner and Ziv; for z(2) = infinity and general given R(2), the minimum R(1) is that of my generalization of the theorem of Wyner and Ziv.

First-Grade Entry Age in a Sample of Children Labeled Learning Disabled

First-grade entry ages were determined for all LD children in grades one through twelve in a large special education cooperative. The children were classified as early, medium, or late entering. U.S. Census data were consulted to determine the expected frequencies in each of these categories. It was found that there were more early-entering and fewer late-entering children in this sample than would be expected if entry age were a chance variable. Though more research is needed, the results of this study suggest the possibility that children who enter first grade early may be more likely to be labeled LD than children who enter when they are older.

Asymptotically efficient estimators when the densities of the observations have discontinuities

Let X1, X2, . . . be independent chance variables with a common density function f(x, 000) at the point x; the density function (d.f.) depends on the value 000 of the real parameter 0, which is unknown to the statistician. In [1] Ibragimov and Hasminski obtained the asymptotic distribution of a stochastic process (similar to that in (4.1) below) naturally connected with the sequence X1, X2, . . . , and of certain functionals of this process. This was done under more or less classical " r e gu l a r " conditions on f ( . , .). A special case of this result is the asymptotic normality of the maximum likelihood (m.1.) estimator. (Throughout this paper we do not distinguish between "es t ima to r" and "sequence of estimators." No confusion will ever be caused thereby.) Since Fisher 's famous paper on the m.1. estimator, asymptotic efficiency has usually been defined as the property of having minimum variance of the limiting normal distribution, among all estimators with limiting normal distribution (with the standard normalization). This definition is already inadequate in the classical " r egu l a r " case, because it limits the competition to be an efficient estimator to estimators which have a l imiting normal distribution. I t fails completely outside the regular case, because then asymptotic normality is also very exceptional. In [11] and [12] (see also [7]) L. Weiss and the present author introduced a notion of asymptotic efficiency (with respect to a set R) which does not have these inadequacies (or other inadequacies of the classical theory). I t also permits the statistician to choose his own loss function, via the set R or directly (see [12]). If, now, one requires f to satisfy the conditions of the (classical) regular case, and, if in addition (what this theory does not require), one admits to competition only asymptotically normal estimators, and chooses R=(--r, r) to be any interval centered at the origin, this notion of efficiency specializes to