An information integration account for causal generalizations in uncertain causal networks

In reasoning about common cause networks, given that a cause generates an effect, people often need to infer how likely the cause generate another effect. This causal generalization question has not systematically been investigated in previous research. We propose the information integration account for causal generalizations in uncertain casual networks with dichotomized continuous variables. It predicts that causal generalization is the joint function of conditional probabilities of causal links and cause strength indicated by the proportion of present collateral effects. Two experiments investigated causal generalizations in uncertain causal networks with and without probability distributions, respectively. It was found that in the presence of probability distributions there was the joint effect of conditional probability and cause strength on causal generalization; in the absence of probability distributions causal generalization depend only on cause strength. The overall response pattern favors the information integration account over the other alternative accounts.

Evaluation of crop growing periods at some stations in northest Brazil

Results of a study of crop .growing periods at some stations in northeast Brazil are presented in this paper. Daily soil moisture values for a minimum period of 25 years are evaluated by means of a simple soil moisture model using temperature and precipitation data. A first order Markov chain model is applied to the soil moisture data and initial and conditional probabilities of wet and dry soil days are obtained. Soil moisture averages and probabilities are used to evaluate crop growing periods at the stations. The effect of uncertainties in the model parameters on the estimated growing periods is investigated.

Update of Prior Probabilities by Minimal Divergence

The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information. The optimal update is obtained by minimizing either the Hellinger distance or the quadratic Bregman divergence. The results obtained by the two methods differ. Updates with information about conditional probabilities are considered as well.

Estimation of the Conditional Probability Using a Stochastic Gradient Process

The use of conditional probabilities has gained in popularity in various fields such as medicine, finance, and imaging processing. This has occurred especially with the availability of large datasets that allow us to extract the full potential of the available estimation algorithms. Nevertheless, such a large volume of data is often accompanied by a significant need for computational capacity as well as a consequent compilation time. In this article, we propose a low-cost estimation method: we first demonstrate analytically the convergence of our method to the desired probability and then we perform a simulation to support our point.

INDICATORS OF SA-19 “GRISON” ANTI-AIRCRAFT GUN MISSILE SYSTEM EFFICIENCY WHEN FIRING AT NONTYPICAL TARGETS OF THE ROCKET AND ARTILLERY WEAPONS

In modern conditions of combat use the SA-19 “Grison” anti-aircraft gun missile system fires at small targets (drones) and typical targets (helicopters and attack aircraft), so a number of problems arise. In particular, they include: finding the value of the probabilities of hitting the target with n shots and one shot; assessing the effectiveness of the SA-19 “Grison” platoon‟s concentrated fire on a single target; estimating errors of missile guidance and warhead detonation system; estimating the values of conditional probabilities of hitting a target with a single missile, depending on the value of particular mishit. When calculating the slant range to the far edge of the SA-19 “Grison” weapon's kill zone under different conditions of use, factors that reduce these ranges should be taken into account. An analysis of the main studies and publications presented in [1-9] does not make it possible to determine the performance of missile and artillery weapons in shooting at small-size targets. This literature provides general approaches to solving this problem. The purpose of this article is to develop a model for calculating the values of conditional probabilities of destruction of small targets, to form the best options for repelling an enemy‟s air strike, as well as to justify the general directions of improvement of weapon‟s elements.

Rescaled range analysis and conditional probability-based probe into the intrinsic pattern of rainfall over North Mountainous India

In work reported here, we have explored rainfall over North Mountainous India for pre-monsoon (MAM), Indian summer monsoon (JJAS), post-monsoon (OND) and Annual. The dependence of JJAS on MAM and OND on JJAS has been explored through conditional probabilities utilizing frequency distribution. An autocorrelation structure has shown that a low lag-1 autocorrelation coefficient characterizes all the time series. We have implemented rescaled range analysis. Through Hurst's exponent and fractal dimension, we have observed that the MAM time series of rainfall over North Mountainous India has a smooth trend and low volatility. We have further observed that for MAM and JJAS, we have , and D is closer to 1 than to 2. However, we have further observed that for OND and Annual rainfall over North Mountainous India and . Therefore, these two time series have been characterized by high volatility and randomness.

The frequent insignificance of a “significant” P-value

Null hypothesis significance testing (NHST) and p-values are widespread in the cardiac surgical literature but are frequently misunderstood and misused. The purpose of the review is to discuss major disadvantages of p-values and suggest alternatives. We describe diagnostic tests, the prosecutor’s fallacy in the courtroom, and NHST, which involve inter-related conditional probabilities, to help clarify the meaning of p-values, and discuss the enormous sampling variability, or unreliability, of p-values. Finally, we use a cardiac surgical database and simulations to explore further issues involving p-values. In clinical studies, p-values provide a poor summary of the observed treatment effect, whereas the three- number summary provided by effect estimates and confidence intervals is more informative and minimises over-interpretation of a “significant” result. P-values are an unreliable measure of strength of evidence; if used at all they give only, at best, a very rough guide to decision making. Researchers should adopt Open Science practices to improve the trustworthiness of research and, where possible, use estimation (three-number summaries) or other better techniques.

Uncertainty in Building Inspection and Diagnosis: A Probabilistic Model Quantification

In the field of building inspection and diagnosis, uncertainty is common and surveyors are aware of it, although it is not easily measured. This research proposes a model to quantify uncertainty based on the inspection of rendered façades. A Bayesian network is developed, considering three levels of variables: characteristics of the building, façade and exposure conditions; causes of defects; and defects. To compute conditional probabilities, the results of an inspection campaign from the literature are used. Then, the proposed model is validated and verified using inspection results from another sample, the combination of a strength-of-influence diagram and sensitivity analysis and the application of the model to a case study. Results show that the probabilities computed by the model are a reasonable representation of the hesitancy in decision making during the diagnosis process based only on visual observation. For instance, design and execution errors show lower probabilities due to not being verifiable a posteriori without detailed documentation. The proposed model may be extended and replicated for other building materials in the future, as it may be a useful tool to improve the perception of uncertainty in a key stage of building maintenance or rehabilitation.

Assessment of the possible amount of harm in case of emergency discharges of bulk cargo from ships

При прогнозировании последствий аварий с участием транспортных судов существует необходимость в получении оперативных оценок размера вреда и длительности воздействия загрязняющих веществ в водных объектах для определения уровня реагирования и разработки защитных мероприятий. Наибольшую экологическую опасность представляют сбросы следующих грузов: удобрений, нефтепродуктов, соли, соды, серы, угля. В статье решается задача по определению возможного размера вреда при аварийных сбросах не нефтеналивных грузов с судов в Волжском бассейне внутренних водных путей. Для этого создана база данных по транспортным происшествиям с участием сухогрузных судов за период наблюдения с 2007 по 2018 гг., выполнено определение гидрологических условий распространения загрязняющих веществ и оценка их концентраций в воде для расчета длительности воздействия. Методика оценки возможного размера вреда водным объектам при сбросе сыпучих грузов при транспортных происшествиях основывается на учете агрессивности ингредиентов различных видов грузов и условных вероятностей их сброса, времени и места сброса, длительности воздействия. В результате выполненных исследований разработано математическое описание для оценки возможного размера вреда водным объектам от сбросов сыпучих грузов при транспортных происшествиях; создана классификация участков концентрации транспортных происшествий по уровню возможного размера вреда водным объектам при сбросе сыпучих грузов; получено уравнение связи размеров вреда при сбросе сыпучих грузов и грузоподъемности судна (массы сброса). When predicting the consequences of accidents involving transport vessels, there is a need to obtain operational estimates of the amount of harm and the duration of exposure to pollutants in water bodies to determine the level of response and develop protective measures. The greatest environmental hazard is posed by the discharges of the following cargoes: fertilizers, oil products, salt, soda, sulfur, coal. The article solves the problem of determining the possible amount of harm in case of emergency discharges of non-oil cargo from ships in the Volga basin of inland waterways. For this, a database on transport accidents involving dry cargo vessels was created for the observation period from 2007 to 2018, the hydrological conditions of the spread of pollutants were determined and their concentrations in water were estimated to calculate the duration of exposure. The methodology for assessing the possible amount of harm to water bodies during the discharge of bulk cargo in transport accidents is based on taking into account the aggressiveness of the ingredients of various types of cargo and the conditional probabilities of their discharge, the time and place of discharge, and the duration of exposure. As a result of the research carried out, a mathematical description was developed to assess the possible extent of damage to water bodies from discharges of bulk cargo during transport accidents; a classification of areas of concentration of traffic accidents was created according to the level of possible damage to water bodies during the discharge of bulk cargo; an equation was obtained for the relationship between the size of damage during the discharge of bulk cargo and the carrying capacity of the vessel (discharge mass).