Objective Probability Research Articles

Objective priors for the number of degrees of freedom of a multivariate [formula omitted] distribution and the [formula omitted]-copula

An objective Bayesian approach to estimate the number of degrees of freedom (ν) for the multivariate t distribution and for the t-copula, when the parameter is considered discrete, is proposed. Inference on this parameter has been problematic for the multivariate t and, for the absence of any method, for the t-copula. An objective criterion based on loss functions which allows to overcome the issue of defining objective probabilities directly is employed. The support of the prior for ν is truncated, which derives from the property of both the multivariate t and the t-copula of convergence to normality for a sufficiently large number of degrees of freedom. The performance of the priors is tested on simulated scenarios 11The R codes and the replication material are available as a supplementary material of the electronic version of the paper.and on real data: daily logarithmic returns of IBM and of the Center for Research in Security Prices Database.

BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc

Abstract BANYAN Σ is a new Bayesian algorithm to identify members of young stellar associations within 150 pc of the Sun. It includes 27 young associations with ages in the range ∼1–800 Myr, modeled with multivariate Gaussians in six-dimensional (6D) XYZUVW space. It is the first such multi-association classification tool to include the nearest sub-groups of the Sco-Cen OB star-forming region, the IC 2602, IC 2391, Pleiades and Platais 8 clusters, and the ρ Ophiuchi, Corona Australis, and Taurus star formation regions. A model of field stars is built from a mixture of multivariate Gaussians based on the Besançon Galactic model. The algorithm can derive membership probabilities for objects with only sky coordinates and proper motion, but can also include parallax and radial velocity measurements, as well as spectrophotometric distance constraints from sequences in color–magnitude or spectral type–magnitude diagrams. BANYAN Σ benefits from an analytical solution to the Bayesian marginalization integrals over unknown radial velocities and distances that makes it more accurate and significantly faster than its predecessor BANYAN II. A contamination versus hit rate analysis is presented and demonstrates that BANYAN Σ achieves a better classification performance than other moving group tools available in the literature, especially in terms of cross-contamination between young associations. An updated list of bona fide members in the 27 young associations, augmented by the Gaia-DR1 release, as well as all parameters for the 6D multivariate Gaussian models for each association and the Galactic field neighborhood within 300 pc are presented. This new tool will make it possible to analyze large data sets such as the upcoming Gaia-DR2 to identify new young stars. IDL and Python versions of BANYAN Σ are made available with this publication, and a more limited online web tool is available at http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php.

CT Prediction Model for Major Arterial Injury after Blunt Pelvic Ring Disruption.

Purpose To develop and test a computed tomography (CT)-based predictive model for major arterial injury after blunt pelvic ring disruptions that incorporates semiautomated pelvic hematoma volume quantification. Materials and Methods A multivariable logistic regression model was developed in patients with blunt pelvic ring disruptions who underwent arterial phase abdominopelvic CT before angiography from 2008 to 2013. Arterial injury at angiography requiring transarterial embolization (TAE) served as the outcome. Areas under the receiver operating characteristic (ROC) curve (AUCs) for the model and for two trauma radiologists were compared in a validation cohort of 36 patients from 2013 to 2015 by using the Hanley-McNeil method. Hematoma volume cutoffs for predicting the need for TAE and probability cutoffs for the secondary outcome of mortality not resulting from closed head injuries were determined by using ROC analysis. Correlation between hematoma volume and transfusion was assessed by using the Pearson coefficient. Results Independent predictor variables included hematoma volume, intravenous contrast material extravasation, atherosclerosis, rotational instability, and obturator ring fracture. In the validation cohort, the model (AUC, 0.78) had similar performance to reviewers (AUC, 0.69-0.72; P = .40-.80). A hematoma volume cutoff of 433 mL had a positive predictive value of 87%-100% for predicting major arterial injury requiring TAE. Hematoma volumes correlated with units of packed red blood cells transfused (r = 0.34-0.57; P = .0002-.0003). Predicted probabilities of 0.64 or less had a negative predictive value of 100% for excluding mortality not resulting from closed head injuries. Conclusion A logistic regression model incorporating semiautomated hematoma volume segmentation produced objective probability estimates of major arterial injury. Hematoma volumes correlated with 48-hour transfusion requirement, and low predicted probabilities excluded mortality from causes other than closed head injury. © RSNA, 2018 Online supplemental material is available for this article.

Risk adaptation and emotion differentiation: An experimental study of dynamic decision-making

We report a study of risky decision-making in a dynamic risk environment, looking in particular at idea that variations in risk preferences over time are subject to both risk adaptation and ability to differentiate negative emotions. In a between-group experiment, 175 participants completed 20 binary project investment decisions under three objective probabilities of success conditions (20%, 50%, and 80%). The results showed that participants’ risk-taking increased at Time 2 only when risky projects have a medium-to-high chance of success (50% or 80%) at Time 1. We also found that participants who could differentiate their negative emotions under the condition with high favorability of risk-taking (80%), achieved higher returns, suggesting that negative emotion differentiation provides emotional information to capture the pattern of decision trials in the environment more favorable to risk-taking success. We conclude with a discussion of the theoretical and practical contributions of our findings for individuals and firms.

Ground filtering and DTM generation from DSM data using probabilistic voting and segmentation

ABSTRACTAutomated digital terrain model (DTM) generation from remotely sensed data has gained wide application areas due to increased sensor resolution. In this study, a novel ground filtering and segmentation method is proposed for digital surface model (DSM) data. The proposed method starts with extracting DSM feature points. These are used in a probabilistic framework to generate a non-ground object probability map in spatial domain. Modes of this map are used as seed points in a novel segmentation method based on morphological operations. This leads to ground filtering and DTM generation. The method is tested on three different data sets. Two of these originate from light detection and ranging (lidar) sensors, where resulting kappa coefficient () range mostly higher than 95% for differently structured urban areas. Also, the visual appearance of the generated DTM exhibits obvious improvements over all other investigated methods. The third data set is a DSM obtained from WorldView-2 stereo image pairs. Also here, we compare our results with three different methods in the literature. Although the DSM quality is much lower, more than 85% of can be reached by the proposed method, showing its superiority over other methods. Overall experimental results show that the proposed method can be used reliably for DTM generation. The results also indicate that the method has prominent advantages in comparison to established methodologies in terms of robustness in handling urban areas of different properties. Moreover, there are only few parameters to adjust in the proposed method, and these are independent of the object size in DSM data.

Моделі рефлективізації контамінованої волітивності у діалогічному мовленні (на матеріалі висловлень-квестивів іспанською мовою)

The article deals with the problem of simulation of the model of reflectivisation of contaminated volition. The article offers a new approach towads the concept of "contaminated volition" under the criteria of differentiation of lacunae in the epistemic domain of the subject of contaminated volition and its affective states. There have been differentiated certain types of consciousness – the epistemic sphere, which acts as the authority of the modal estimation, which is the basis of the simulation of the reflection of contaminated volition. According to the analysis of the correlations between the nature of the contamination of the volition, the individual sphere of consciousness, causing the modal estimation, and the means of their speech expression, the study discusses the biases in the non-prototype modal estimation of the volition through a series of gradual transitions in the continuum of modal meanings: the possibility – objective probability – subjective probability (chance) – a negative possibility.

On the Immense and Severe Confusions and Muddles About the Nature Ofsubjective and Objective Probability in the Theory of Rational Expectations: Asubjective Probability (Distribution) Can Never Become an Objective Probability(distribution) or a True or a Correct Probability

On the Immense and Severe Confusions and Muddles About the Nature Ofsubjective and Objective Probability in the Theory of Rational Expectations: Asubjective Probability (Distribution) Can Never Become an Objective Probability(distribution) or a True or a Correct Probability

Recurrent Algorithms of Structural Classification Analysis for Complex Organized Information

For the structural classification analysis of complex organized information, we propose to use recurrent algorithms of stochastic approximation type. We introduce classification quality functionals that depend on non-normalized and zero moments of probability distribution functions for the probability of sample objects appearing in the classes, as well as the type of optimal classification. We propose a new classification algorithm for this type of classification quality criteria and prove a theorem about its convergence that ensures the stationary value of the corresponding functional. We show that the proposed algorithm can be used to solve a wide class of problems in structural classification analysis.

Lewisian analysis of objective probability: An origin of the undermining futures

David Lewis? interpretation of objective probability has two essential parts: Humean supervenience and Best system of laws. According to his interpretation, probabilities, along with the other nomic phenomena, supervene on the actual facts. Lewis also famously formulated the Principal Principle, which should show the connection between objective and subjective probabilities. However, years later, Hall, Thau and Lewis himself came to conclusion that the principle and Lewis? interpretation of probability are not compatible. The main reason for that is the problem of so called undermining futures: Lewis named the problem ?Big Bad Bug?. A popular way to solve the problem was to change the principal principle. In this article, I will argue that the origin of the problem is not compatibility of the principle and Lewis?s interpretation of probability, but that the problem is in the interpretation itself. Changing the principle, I argue, will not conclusively solve the problem.

Construction and Application of Water Resource Accounting Information System in Taiwan

The paper presents a model of reliability which captures the process by which reliability is actually determined more accurately than the conventional analysis. In contrast to that conventional analysis, which is based on the characteristics approach, the model of this paper defines reliability as the objective probability of product failure, not as a characteristic of individual goods. Reliability, thus defined, is treated as a choice variable of the firm. The resulting model is applied to a monopolist subject to a price cap. The monopolist can vary reliability and the terms of a warranty or compensation deal in response to price-capping. The monopoly outcome, price-capped monopoly outcome, and Pareto-efficient outcome are compared. The model provides a theoretical explanation of some empirical results in the literature on electricity regulation.

Tear Down this Wall Street: Anti-Market Rhetoric, Motivated Beliefs, and Investment

Anti-finance rhetoric pre-dates modern capitalism, is diffused in capitalistic economies, and peaks during economic crises. Is anti-finance rhetoric an inert cultural byproduct of crises, or does it in turn affect economic decision-making? If it does, through which channels? To avoid the confounding shocks that accompany economic crises, I manipulate exposure to anti-finance rhetoric in an artefactual field experiment. Subjects exposed to anti-finance rhetoric invest less often and less money in risky opportunities framed as stock investments relative to controls. Risk aversion does not change with exposure, whereas exposed subjects distrust the financial sector more than others. Consistent with ideologically-motivated cognition, educated, experienced, and female subjects react more than others. Treated subjects react to positive news but not to negative news about investment outcomes---anti-finance rhetoric might make them expect bad investment outcomes despite knowing the objective probabilities of each potential payoff.

Perceptions of randomness in binary sequences: Normative, heuristic, or both?

When people consider a series of random binary events, such as tossing an unbiased coin and recording the sequence of heads (H) and tails (T), they tend to erroneously rate sequences with less internal structure or order (such as HTTHT) as more probable than sequences containing more structure or order (such as HHHHH). This is traditionally explained as a local representativeness effect: Participants assume that the properties of long sequences of random outcomes—such as an equal proportion of heads and tails, and little internal structure—should also apply to short sequences. However, recent theoretical work has noted that the probability of a particular sequence of say, heads and tails of length n, occurring within a larger (>n) sequence of coin flips actually differs by sequence, so P(HHHHH) <P(HTTHT). In this alternative account, people apply rational norms based on limited experience. We test these accounts. Participants in Experiment 1 rated the likelihood of occurrence for all possible strings of 4, 5, and 6 observations in a sequence of coin flips. Judgments were better explained by representativeness in alternation rate, relative proportion of heads and tails, and sequence complexity, than by objective probabilities. Experiments 2 and 3 gave similar results using incentivized binary choice procedures. Overall the evidence suggests that participants are not sensitive to variation in objective probabilities of a sub-sequence occurring; they appear to use heuristics based on several distinct forms of representativeness.

Spatiotemporal salient object detection by integrating with objectness

This paper proposes a novel spatiotemporal salient object detection method by integrating saliency and objectness, for videos with complicated motion and complex scenes. The initial salient object detection result is first built upon both saliency map and objectness map. Afterwards, the region size of salient object is adjusted to obtain the frame-wise salient object detection result by iteratively updating the object probability map, which is the combination of saliency map and objectness map. Finally, in order to enhance the temporal coherence, the sequence-level refinement is performed to generate the final salient object detection result. Experimental results on public benchmark datasets demonstrate that the proposed method consistently outperforms the state-of-the-art salient object detection methods.

Ambiguity and expectation-neglect in dilemmas of interpersonal trust

AbstractRecent research suggests that people discount or neglect expectations of reciprocity in trust dilemmas. We examine the underlying processes and boundary conditions of this effect, finding that expectations have stronger effects on trust when they are made accessible and when they are provided as objective probabilities (Study 1). Objective expectations have stronger effects when they are based on precise, rather than ambiguous, probabilities (Study 2). We also find that trust decisions differ from individual risk-taking decisions: people are more willing to trust, and expectations have stronger effects on trusting behavior (Study 2). These results show that the availability and ambiguity of expectations shape trust decisions, and that people differentially weight expectations in dilemmas of trust and individual risk-taking.

Ecological resilience in lakes and the conjunction fallacy.

There is a pressing need to apply stability and resilience theory to environmental management to restore degraded ecosystems effectively and to mitigate the effects of impending environmental change. Lakes represent excellent model case studies in this respect and have been used widely to demonstrate theories of ecological stability and resilience that are needed to underpin preventative management approaches. However, we argue that this approach is not yet fully developed because the pursuit of empirical evidence to underpin such theoretically grounded management continues in the absence of an objective probability framework. This has blurred the lines between intuitive logic (based on the elementary principles of probability) and extensional logic (based on assumption and belief) in this field.

Scene recognition with objectness

In this paper, we present a feature description method called semantic descriptor with objectness (SDO) for scene recognition. Most existing scene representation methods exploit the characteristics of constituent objects in scenes with inter-class independence, which ignore the negative effects caused by the common objects among different scenes. The generic characteristics of the common objects cause some generality among different scenes, which weakens the discriminative characteristics among scenes. To address this problem, we exploit the correlations of object configurations among different scenes by the co-occurrence pattern of all objects across scenes to choose representative and discriminative objects which enhances the inter-class discriminability. Specifically, we capture the statistic information of objects appearing in each scene to compute the distribution of each object across scenes, which obtains the co-occurrence pattern of objects. Moreover, we represent the image descriptors with the occurrence probabilities of discriminative objects in image patches to eliminate the negative effects of common objects. To make image descriptors more discriminative, we discard the patches with non-discriminative objects to enhance the intra-class generalized characteristics. Experimental results on three widely used scene recognition datasets show that our method outperforms the state-of-the-art methods.

Gain/Loss Framing Effects on Learning in Economic Decision Making Investigated with Eye Tracking

Previous research has revealed a domain-based bias when people estimate payout likelihoods for probabilistic choice options that minimize losses versus those that maximize gains (Kuhnen, 2015). For...

Decision-making and Related Processes in Internet Gaming Disorder and Other Types of Internet-Use Disorders

The review aims to characterize decision-making in individuals with symptoms of Internet gaming disorder (IGD) and other types of Internet-use disorders. We therefore discuss both theories of decision-making and theoretical models of Internet-use disorders as well as recent studies which investigated decision-making in these addictive behaviors. Studies from 2012 to 2017 demonstrated that individuals with symptoms of IGD show riskier behavior, tend to disregard objective probabilities, display reduced feedback processing, and have a preference for immediate rewards. These behaviors are related to increased reward sensitivity and reduced executive/inhibitory control on behavioral and brain levels. Risky and short-termly oriented decisions may be major aspects in the development and maintenance of IGD and other Internet-use disorders. Dual-process models of decision-making can explain the addictive behavior by interactions between immediate reward expectation, specific predisposing factors, and situational aspects. These interactions make it increasingly likely that short-term-oriented impulses towards the use of specific Internet applications overwhelm attempts to reflectively control the behavior.

Object defense with preventive strike and false targets

In this paper, optimal strategies for the defender and the attacker are studied. The defender moves first, allocating its limited resources into three diverse measures: launching a preventive strike, building false targets, and protecting the genuine object. It is assumed that launching a preventive strike will expose the genuine object, thus during this measure the defender will not simultaneously build false targets. The attacker moves after observing the actions taken by the defender, allocating its resources into three measures: protecting its own base from a preventive strike, building false bases, and attacking the genuine object. For each of the defender's given strategies, the attacker tries to maximize the destruction probability of the genuine object. Comparing the expected vulnerability of the object, the defender decides whether to launch a preventive strike or build false targets. The strategies of the attacker and the defender are illustrated with numerical examples, and the optimal strategies are found.

Salient Object Segmentation via Effective Integration of Saliency and Objectness

This paper proposes an effective salient object segmentation method via the graph-based integration of saliency and objectness. Based on the superpixel segmentation result of the input image, a graph is built to represent superpixels using regular vertex, background seed vertex with the addition of a terminal vertex. The edge weights on the graph are defined by integrating the difference of appearance, saliency, and objectness between superpixels. Then, the object probability of each superpixel is measured by finding the shortest path from the corresponding vertex to the terminal vertex on the graph, and the resultant object probability map can generally better highlight salient objects and suppress background regions compared to both saliency map and objectness map. Finally, the object probability map is used to initialize salient object and background, and effectively incorporated into the framework of graph cut to obtain the final salient object segmentation result. Extensive experimental results on three public benchmark datasets show that the proposed method consistently improves the salient object segmentation performance and outperforms the state-of-the-art salient object segmentation methods. Furthermore, experimental results also demonstrate that the proposed graph-based integration method is more effective than other fusion schemes and robust to saliency maps generated using various saliency models.