Measure Of Likelihood Research Articles

Uncertainty analysis of HEC-HMS model using the GLUE method for flash flood forecasting of Mekerra watershed, Algeria

In this paper, the lumped quasi-distributed hydrological model HEC HMS is used to simulate the rainfall–runoff process of the Mekerra watershed, located in the northwest of Algeria. The model parameters’ uncertainty and the predictive intervals were evaluated with the generalized likelihood uncertainty estimation (GLUE) approach. According to the results, good simulations were obtained with different values of variables for many sets of parameters generated randomly by the Monte Carlo procedure, which is known as Equifinality. After the analysis, only the hydraulic conductivity at saturation parameter appears well defined, taking values within a limited range. In addition, results indicated that combinations of likelihood measures associated with multiple and different periods of observations reduce a posterior uncertainty of estimated parameters and predictive intervals in some degree. Overall, the GLUE analysis showed that there is a significant uncertainty associated with hydrological modelling of watershed Mekerra, to a great extent due to multiple sources of errors.

Goal Fluency, Pessimism and Disengagement in Depression.

Despite the development of prominent theoretical models of goal motivation and its importance in daily life, research has rarely examined goal dysregulation processes in clinical depression. Here we aimed to investigate problematic aspects of goal regulation in clinically depressed adults, relative to controls. Depressed participants (n = 42) were recruited from two Improving Access to Psychological Therapy clinics in north-west England. Control participants (n = 51) were recruited from the same region. Participants generated personal approach goals (e.g., improve my marathon time) and avoidance goals (e.g., avoid getting upset over little things) and completed self-report measures of goal attainment likelihood and depressive symptoms. Participants also completed a measure of ease of disengagement from unattainable goals and re-engagement with new goals. Compared to controls, depressed participants reported fewer approach goals (but not more avoidance goals), rated their approach goal (rewarding) outcomes as less likely to happen and avoidance goal (threatening) outcomes as more likely to happen. Depressed participants also reported greater ease of disengagement from unattainable goals and more difficulty re-engaging with new goals than controls. Our findings extend current knowledge of the psychopathology of depression from a goal regulation perspective, suggesting that pessimism around goal pursuit accompanies fewer approach goal pursuits and a general tendency to disengage when difficulties are encountered.

E-cigarette use 1 year later in a population-based prospective cohort

BackgroundPopulation prevalence estimates of electronic nicotine delivery system (ENDS) use range considerably based on the operational definition of ‘use’. Recently, we investigated the utility of ‘use frequency’ for restricting prevalence...

Defining and measuring student adjustment-seeking behavior

Purpose The purpose of this paper is to describe and define the concept of student adjustment-seeking behavior, differentiating it from the related behaviors of compliance-seeking, and help-seeking. Design/methodology/approach Based on a survey of college students, documentation is provided as to the frequency of occurrence of student adjustment-seeking behavior. Also presented is empirical, albeit exploratory, evidence of a statistically significant correlation between self-reports of prior adjustment-seeking behavior, and adjustment-seeking likelihood. Findings In support of the study hypothesis, a moderately high, bivariate correlation was observed between adjustment-seeking likelihood and prior adjustment-seeking behavior. Originality/value Further attention to the topic and measurement of adjustment-seeking behavior would seem to be justified and continued efforts to validate the measure of adjustment-seeking likelihood may be fruitful in efforts to better understand adjustment-seeking behavior. Research recommendations, practical applications, and study limitations are discussed.

The joint effects of regulatory focus and argument strength of product related information on choice behaviour

Past research studying the effects of regulatory fit on consumer choice decisions indicate that consumers possess favourable attitude towards products that are compatible with their regulatory orientations. The current research contends that regulatory fit by itself may not be sufficient for attracting consumers to purchase a product. Consumers will show a favourable attitude towards products that match their regulatory orientations only when the information related to the products that match their regulatory focus has a strong argument quality. The effects of regulatory fit will be reversed if the argument quality becomes weak. Findings from a series of experiments support this hypothesis. Regulatory focus was used as a chronic variable and a situational variable (between subjects) in the experiments. Argument strength was used as a between subjects variable. Choice favourability and choice likelihood measures were used as the dependent variables.

The personal values and study skills can be better predictors of college success than personality traits.

The Big Five Model of Personality has limited support in the field of neuroscience and challenged by low factor–criterion correlation (0.2-0.3, N=142). To overcome these limitations, we previously attempted to create an alternative model. For that model we choose the traits that were consistent with the modern ideas of brain organization and brain functioning. This model had five personality parameters 1) Fear tolerance, the ability to overcome fears and the resistance to developing them in the first place; 2) Impulse tolerance, the ability to resist immediate impulses to gratify desires or correct homeostatic imbalances; 3) Emotional tolerance, the ability to resist the effects of emotion in favor of rationality; 4) Traditional tendency, the measure of likelihood that either a traditional or novel option will hold more sway; and 5) Self-conception, the rewarding value we assign to projected outcomes. Here, we attempted to see if the new model can serve as a better predictor of behaviors, more specifically the college success. We developed a short questionnaire based on the traits proposed by the model (24 items total), asked anonymous participants attending freshman or senior classes to complete it, and evaluate the responses. We clearly recognized the piloting nature of the administrated questions and a potential impact on assessment of low inventory questionnaires, but it was immediately clear that the questions addressing personality traits had limited link to the seniority of the students. In contrast, responses of students of different seniority had been clearly dissociated every time when relevant to college success personal values and skills were evaluated (correlation 0.24-0.47, N=24, p<0.05). Although more research is needed, the findings indicate that personal values and study skills are much better predictors of college success than personality traits.

On the road to precision, there is more practical medicine to be implemented

Estimates are that of the annual global burden of 1.5 million new cases of breast cancer, two-thirds have hormone receptor positive tumors; a majority of these women come from low- and middle-income countries. For adjuvant patients with hormone receptor positive tumors, a major goal is identification of a “precision medicine”, implying a genomic, test whose application will allow identification of those whose systemic treatment can be hormonal therapy alone. Such tests in current use are very expensive and thus in the foreseeable future are out of reach of most women who pay out of pocket.For some time it has been evident that quantitative scoring of tumors for intensity and prevalence of tumor-cell staining for estrogen or progesterone receptor (ER or PR) expression (the commonest system was first described by Allred and thus provides “Allred” scores) gives an inexpensive measure of likelihood of response to hormonal therapies – a different predictive, precision medicine tool. Majorities of hormone receptor positive tumors (one third of all patients) have “Allred” scores of 6–8 (versus scores of 3–5) for both ER and PR and these tumor-bearing patients benefit significantly more from hormonal treatments than their lowering scoring-afflicted women. When ER and PR quantitative intensity and prevalence scoring is combined with Her-2/neu testing and careful tumor histologic grading, luminal A and B type tumors can be well-defined and gene-expression testing adds little practical predictive information.For women with hormone receptor positive tumors, high quality, cost-effective “precision medicine” is available without tumor gene-expression testing.

Acceptability of Three Novel HIV Prevention Methods Among Young Male and Transgender Female Sex Workers in Puerto Rico.

Sex workers need HIV-prevention methods they can control and incorporate easily in their work. We studied the acceptability of three methods: HIV self-test use with clients, oral pre-exposure prophylaxis (PrEP), and rectal microbicide gel. Four male and eight transgender female (TGF) sex workers in Puerto Rico completed a baseline survey with a quantitative measure of likelihood of use. From them, one male and four TGF also completed a 12-week study of rectal microbicide placebo gel use prior to receptive anal intercourse with male clients and evaluated via qualitative in-depth interviews and follow-up quantitative assessments how each method could be incorporated into their work. Most were interested in a rectal microbicide gel and able to use it covertly with clients. Challenges to using the HIV self-test with clients included the potential for both breach of confidentiality and confronting violent situations. Participants also expressed interest in oral PrEP, but raised concerns about side effects.

Measuring technological distance for patent mapping

Recent works in the information science literature have presented cases of using patent databases and patent classification information to construct network maps of technology fields, which aim to aid in competitive intelligence analysis and innovation decision making. Constructing such a patent network requires a proper measure of the distance between different classes of patents in the patent classification systems. Despite the existence of various distance measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing patent technology network maps. This ambiguity has limited the development and applications of such technology maps. Herein, we propose to compare alternative distance measures and identify the superior ones by analyzing the differences and similarities in the structural properties of resulting patent network maps. Using United States patent data from 1976 to 2006 and the International Patent Classification (IPC) system, we compare 12 representative distance measures, which quantify interfield knowledge base proximity, field‐crossing diversification likelihood or frequency of innovation agents, and co‐occurrences of patent classes in the same patents. Our comparative analyses suggest the patent technology network maps based on normalized coreference and inventor diversification likelihood measures are the best representatives.

Optimization in Online Content Recommendation Services: Beyond Click-Through Rates

A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a “browsing path” along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles. Whereas CTR is a measure of instantaneous click likelihood, we analyze the performance improvement that one may achieve by some lookahead that accounts for the potential future path of users. To that end, by using some data of user path history at major media sites, we introduce and derive a representation of content along two key dimensions: clickability, the likelihood to click to an article when it is recommended; and engageability, the likelihood to click from an article when it hosts a recommendation. We then propose a class of heuristics that leverage both clickability and engageability, and provide theoretical support for favoring such path-focused heuristics over myopic heuristics that focus only on clickability (no lookahead). We conduct a live pilot experiment that measures the performance of a practical proxy of our proposed class, when integrated into the operating system of a worldwide leading provider of content recommendations, allowing us to estimate the aggregate improvement in clicks per visit relative to the CTR-driven current practice. The documented improvement highlights the importance and the practicality of efficiently incorporating the future path of users in real time.

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port-specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed-effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one-tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports.

Classification of ABO3 perovskite solids: a machine learning study.

We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.

An Apparatus and Method for Real-Time Stacked Sheets Counting With Line-Scan Cameras

To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <;0.2 mm.

Autonomous detection and tracking under illumination changes, occlusions and moving camera

In this paper, an autonomous multiple target detection and tracking technique for dynamic scenes that are influenced by illumination variations, occlusions and camera instability is proposed. The framework combines a novel Dynamic Reverse Analysis (DRA) approach with an Enhanced Rao-Blackwellized Particle Filter (E-RBPF) for multiple target detection and tracking, respectively. The DRA method, in addition to providing accurate target localization, presents the E-RBPF scheme with costs associated with the differences in intensity caused by illumination variations between consecutive frame pairs in any video of a dynamic scene. The E-RBPF inherently models these costs, thus allowing the framework to (a) adapt learning parameters, (b) distinguish between camera-motion and object-motion, (c) deal with sample degeneracy, (d) provide appropriate appearance compensation during likelihood measurement and (e) handle occlusion. The proposed detect-and-track method when compared against other competing baseline techniques has demonstrated superior performance both in accuracy and robustness on challenging videos from publicly available datasets.

Sparse representation-based human detection: a scale-embedded dictionary approach

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.

Estimation of structural changes in nonlinear time series models by using particle filters and genetic programming

SummarySeveral works have demonstrated detection of changes of state equations (called structural changes) based on statistical measures but have given no suggestions regarding the functional forms of the state equations after changes. This paper deals with the estimation of structural changes in nonlinear time series models by using particle filters, genetic programming (GP), and its applications. We consider the problems of state estimation from the observed time series that are generated based on nonlinear state equations. It is assumed that structural changes can be detected by some measure of likelihood and that the state equation after changes is modified from its current functional form. Individuals corresponding to functional forms in the GP pool are generated at random, and we apply the crossover operation between the current functional form and the individuals by giving possible multiple functional forms. Then, we have the optimal functional form among the possible functional forms generated by GP from the current form. As an application, we show the estimation of structural change for an artificially generated time series and also discuss the estimation of functional forms for a real economic time series before and after structural changes. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Interim treatment: Bridging delays to opioid treatment access

ObjectiveDespite the undisputed effectiveness of agonist maintenance for opioid dependence, individuals can remain on waitlists for months, during which they are at significant risk for morbidity and mortality. To mitigate these risks, the Food and Drug Administration in 1993 approved interim treatment, involving daily medication+emergency counseling only, when only a waitlist is otherwise available. We review the published research in the 20years since the approval of interim opioid treatment. MethodsA literature search was conducted to identify all randomized trials evaluating the efficacy of interim treatment for opioid-dependent patients awaiting comprehensive treatment. ResultsInterim opioid treatment has been evaluated in four controlled trials to date. In three, interim treatment was compared to waitlist or placebo control conditions and produced greater outcomes on measures of illicit opioid use, retention, criminality, and likelihood of entry into comprehensive treatment. In the fourth, interim treatment was compared to standard methadone maintenance and produced comparable outcomes in illicit opioid use, retention, and criminal activity. ConclusionsInterim treatment significantly reduces patient and societal risks when conventional treatment is unavailable. Further research is needed to examine the generality of these findings, further enhance outcomes, and identify the patient characteristics which predict treatment response.

Confirmation, increase in probability, and partial discrimination: A reply to Zalabardo

There is a plethora of confirmation measures in the literature. Zalabardo considers four such measures: PD (Probability-Difference), PR (Probability-Ratio), LD (Likelihood-Difference), and LR (Likelihood-Ratio). He argues for LR and against each of PD, PR, and LD. First, he argues that PR is the better of the two probability measures. Next, he argues that LR is the better of the two likelihood measures. Finally, he argues that LR is superior to PR. I set aside LD and focus on the trio of PD, PR, and LR. The question I address is whether Zalabardo succeeds in showing that LR is superior to each of PD and PR. I argue that the answer is negative. I also argue, though, that measures such as PD and PR, on one hand, and measures such as LR, on the other hand, are naturally understood as explications of distinct senses of confirmation.

Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment

AbstractThe Beerkan method based on in situ single‐ring water infiltration experiments along with the relevant specific Beerkan estimation of soil transfer parameters (BEST) algorithm is attractive for simple soil hydraulic characterization. However, the BEST algorithm may lead to erroneous or null values for the saturated hydraulic conductivity and sorptivity especially when there are only few infiltration data points under the transient flow state, either for sandy soil or soils in wet conditions. This study developed an alternative algorithm for analysis of the Beerkan infiltration experiment referred to as BEST‐generalized likelihood uncertainty estimation (GLUE). The proposed method estimates the scale parameters of van Genuchten water retention and Brooks–Corey hydraulic conductivity functions through the GLUE methodology. The GLUE method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the goodness‐of‐fit between modelled and observed data. The results showed that using a combination of three different likelihood measurements based on observed transient flow, steady‐state flow and experimental steady‐state infiltration rate made the BEST‐GLUE procedure capable of performing an efficient inverse analysis of Beerkan infiltration experiments. Therefore, it is more applicable for a wider range of soils with contrasting texture, structure, and initial and saturated water content. Copyright © 2015 John Wiley &amp; Sons, Ltd.

An Improved Bayesian Structural Identification Using the First Two Derivatives of Log-Likelihood Measure

The posterior density of structural parameters conditioned by the measurement is obtained by a differential evolution adaptive Metropolis algorithm (DREAM). The surface of the formal log-likelihood measure is studied considering the uncertainty of measurement error to illustrate the problem of equifinality. To overcome the problem of equifinality, the first two derivatives of the log-likelihood measure are proposed to formulate a new informal likelihood measure for the sake of improving the accuracy of the estimator. Moreover, the proposed measure also reduces the standard deviation (uncertain range) of the posterior samples. The benefit of the proposed approach is demonstrated by simulations on identifying the structural parameters with limit output data and noise polluted measurements.