Conditional Probability Of Occurrence Research Articles

Sequential Mapping of Game Patterns in Men and Women Professional Padel Players.

This study analyzed the sequences of actions in professional men and women padel players to identify common game patterns. The sample comprised 17,557 stroke-by-stroke actions (N = 1640 rallies) of the championship World Padel Tour. Multistep Markov chains were used to calculate the conditional probabilities of occurrence of actions during the rally. Results revealed that men's and women's padel is mainly defined by 36 patterns constituting 55% and 63% of all actions in the game, respectively, with the 10 most common sequences accounting for 42% to 45% of the game. There were recurrent technical-tactical actions with specific offensive and defensive functions that were constantly reiterated during the rallies. In men, the use of smash, volley, bandeja, direct, back wall, back-wall lobs, and direct lobs followed a foreseeable pattern up to 8 lags, whereas women described predictable interactions for volley, bandeja, direct, lobs, and direct lobs up to 5 lags and for smash and back wall up to 4 lags. The ability of padel players to recall these patterns and enhance their anticipation skills may potentially improve their performance. These findings contribute to a better knowledge of professional padel game dynamics while providing coaches and players with useful information to optimize training and decision-making strategies.

Wet and Dry Spell Analysis for Crop Planning in Central Plain Zone of Punjab Using Markov Chain Method

A study was conducted during 2017–2018 at department of climate change and agricultural meteorology, PAU, Ludhiana to find out the initial and conditional probability of occurrence of wet and dry spell of rainfall at different locations of Punjab, the analysis was carried out using Markov Chain Method (MCM). The historical data of rainfall for three locations of Punjab viz. Amritsar (1971–2017), Ludhiana (1971–2018) and Patiala (1971–2018) were collected from the Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana and Met Centre, India Meteorological Department, Chandigarh. At Amritsar the chances of occurrence of wet spell were highest during 28th SMW i.e., 72% followed by 68% during 30th and 31st SMW respectively. The conditional probability calculated at Ludhiana showed that the probability of wet spell followed by wet spell was highest (71%) during 28th SMW followed by 66 per cent during 30th and 32nd SMW. At Patiala the probability of wet spell was highest (69%) during 33rd week followed by 63% during 29th SMW. The conditional probability also showed that dry spell followed by dry spell occurred more frequently than wet spell followed by wet spell.

Segmentation for Multimodal Brain Tumor Images Using Dual-Tree Complex Wavelet Transform and Deep Reinforcement Learning.

Image segmentation is an effective tool for computer-aided medical treatment, to retain the detailed features and edges of the segmented image and improve the segmentation accuracy. Therefore, a segmentation algorithm using deep reinforcement learning (DRL) and dual-tree complex wavelet transform (DTCWT) for multimodal brain tumor images is proposed. First, the bivariate concept in DTCWT is used to determine whether the image noise points belong to the real or imaginary region, and the noise probability is checked after calculation; second, the wavelet coefficients corresponding to the region where the noise is located are selected to transform the noise into normal pixel points by bivariate; then, the conditional probability of occurrence of marker points in the edge and center regions of the image is calculated with the target points, and the initial segmentation of the image is achieved by the known wavelet coefficients; finally, the segmentation framework is constructed using DRL, and the network is trained by loss function to optimize the segmentation results and achieve accurate image segmentation. The experiment was evaluated on BraTS2018 dataset, CQ500 dataset, and a hospital brain tumor dataset. The results show that the algorithm in this paper can effectively remove multimodal brain tumor image noise, and the segmented image has good retention of detail features and edges, and the segmented image has high similarity with the original image. The highest information loss index of the segmentation results is only 0.18, the image boundary error is only about 0.3, and F-value is high, which indicates that the proposed algorithm is accurate and can operate efficiently, and has practical applicability.

Motivation(s) from control: response-effect contingency and confirmation of sensorimotor predictions reinforce different levels of selection.

Humans and other animals live in dynamic environments. To reliably manipulate the environment and attain their goals they would benefit from a constant modification of motor-responding based on responses' current effect on the current environment. It is argued that this is exactly what is achieved by a mechanism that reinforces responses which have led to accurate sensorimotor predictions. We further show that evaluations of a response's effectiveness can occur simultaneously, driven by at least two different processes, each relying on different statistical properties of the feedback and affecting a different level of responding. Specifically, we show the continuous effect of (a) a sensorimotor process sensitive only to the conditional probability of effects given that the agent acted on the environment (i.e., action-effects) and of (b) a more abstract judgement or inference that is also sensitive to the conditional probabilities of occurrence of feedback given no action by the agent (i.e., inaction-effects). The latter process seems to guide action selection (e.g., should I act?) while the former the manner of the action's execution. This study is the first to show that different evaluation processes of a response's effectiveness influence different levels of responding.

Conditional probabilities and Bayesian theorem in the study of animal disease introduction. (a didactic approach in environmental risk analysis)

Bayesian analysis is one of the topics included in the subject “Environmental Risk Analysis” taught at the Faculty of Agronomy of the University of Buenos Aires, Argentina. Traditionally, we use a tree of probabilities to organize basic concepts necessary to understand this analysis (for example, conditional probability, excluding events, independence, etc.). Then, we deal with concepts of Bayesian analysis (such as additional conditional probability, prior probabilities, posterior probabilities, and their applications). In this work, we propose to analyze the problem method that starts by analyzing the scenarios.•For that, we begin the analysis by paying attention to those events that we are certain have occurred.•We will separate them according to their possible origins and assign them a probability (conditional).•We will then use the probabilities to weight them, and then find a conditional probability of occurrence.

Modelling Earthquake’s Inter-event Recurrence Intervals (IRIs) in Central India and Adjoining Regions using Weibull Probability Distribution Model – A Zone-Wise Approach

This study analyzes and model zone-wise earthquakes inter-event recurrence interval (IRIs) using a stochastic Weibull probabilistic distribution model. The study region between 19–28◦N and 72–84◦E up to 200 km peripheral boundaries of Central India (Madhya Pradesh) is divided into four zones (Zone South, West, North, and East), respectively, based on different clusters of earthquakes with shared seismo-tectonic regimes. Seismic events’ catalog from different national and international resources for a period of more than 100 years are considered in this study. The seismic data is grouped into two categories based on a different range of earthquake magnitudes (3≤Mw< 4 & 4 ≤ Mw≤ 6). Zone-wise results are produced in the form of hazard curves and conditional probabilities of occurrence for a range of elapsed time from 3 to 30 years from last recorded earthquakes in various zones.

An Intelligent Location Prediction System for Wind Farms Using Belief Networks

An energy crisis is a major problem India is facing today. Alternative energy sources are very important today to overcome the impact of rising oil prices. The main alternative energy source is the power from wind energy sources having 10% in India’s total energy consumption. The wind farm location planning to achieve the best energy output generation is in need of a finding solution. In this article, together with the belief network, a challenge is made exactly to prospects the energy from wind resources with and applied a statistical approach. Soft Computing techniques play a major role in the research applications especially in the multi-disciplinary areas. According to the technological development various soft computing techniques can be used in many areas to analyze the problem including Artificial Neural Network, Fuzzy logic, Adaptive Neuro and machine language. In this study the potential location of wind energy is identified by the belief neural network technique. The basic concept of Bayesian uncertainty treatment is that this analysis analyzes the conditional probability of occurrence. In addition, a particular region’s ecological parameters and environmental issues are also important. The ecological parameters and environmental factors also involved in the wind velocity of a particular region.

Time-dependent probabilistic seismic hazard assessment in Kerman and adjacent areas in the west of Lut block, Central-East Iran

This study is the first attempt to apply fault-based and time-dependent seismicity models cooperatively for probabilistic seismic hazard assessment (PSHA) of the Kerman and adjacent areas (54–59° E, 28.5–34° N). For this study, almost all available literature and databases have been investigated to compile reliable information reported as instrumental, historical, and paleoseismological events, together with the geometry and kinematics information for active faults. This information systematically is integrated and contributed to PSHA. Conditional probabilities of occurrence for earthquakes greater than the maximum earthquake from which its standard deviation is deducted (M > Mmax − σMmax) are computed based on a Brownian passage time (BPT) model. The spatial distribution of the time-dependent peak ground acceleration (PGA) has been calculated, and spectral accelerations have been computed for three selected cities (Kerman, Rafsanjan, Ravar), with 10% probability of exceedance in 50 years. The time-dependent PSHA map for PGA shows variations from − 21 along the Gowk fault to + 32% near the Sabzevaran fault segments in comparison with the corresponding time-independent PGAs. The difference between the time-dependent and time-independent PGAs at most parts of the study area is negligible, especially at sites farther away from the faults, where the differences vary from − 7 to + 7%. Moreover, the difference between the time-dependent and time-independent spectral accelerations for Ravar and Rafsanjan cities is insignificant. However, this difference at periods of 0.1–0.3 s is significant in Kerman city. The results of this study provide a stimulus for considering time-dependency assumption and fault-based approach in future earthquake-resistant constructions required in Kerman and adjacent areas.

Making inferences about non-detection observations to improve occurrence predictions in Venezuelan Psittacidae

SummaryThe global decline in psittacid populations highlights the need for monitoring programmes that allow us to estimate the level of confidence that can be placed in a non-detection observation in order to assess changes in range status. We used the detection/non-detection records for 26 psittacid species detected during the first national bird monitoring programme in Venezuela carried out in 2010 by the Neotropical Biodiversity Mapping Initiative. We fitted occupancy models and evaluate the suitability of the data to explain the lack of detections given the current sampling effort, and the expected occurrence probabilities due to environmental conditions (conditional probability of occurrence; ΨCONDL). We were able to fit reliable models for 13 of the 26 species detected. For Green-rumped ParrotletForpus passerinus, Blue-headed ParrotPionus menstrus, and Orange-winged AmazonAmazona amazonica, the probability of detection (p) under the current sampling effort was too low (> 0.2) in areas where environmental conditions would imply high ΨCONDL(< 0.3). This suggests that sampling effort should be increased to generate reliable estimations of occurrence. In contrast, for Scarlet MacawAra macao, Yellow-crowned AmazonAmazona ochrocephala, Orange-chinned ParakeetBrotogeris jugularisand Brown-throated ParakeetEupsittula pertinaxthe model estimated highp(< 0.3) and low ΨCONDL(> 0.2), suggesting that the species are reliably detected and better models could be obtained by including other predictive variables related to temporal use of resources and habitat heterogeneity. To improve the effectiveness of parrot monitoring programme in Neotropical countries, we suggest increasing the sampling effort, developing several surveys per year, and including variables related with temporal use of resources and habitat heterogeneity.

Copula-based agricultural drought risk of rainfed cropping systems

In a future climate, warmer and drier conditions are expected, and the associated negative impacts in agricultural productions are a major issue. Assessing the risk of drought hazard on agricultural systems is, therefore, of main importance in decision-making, with the aim of mitigating drought-related crop losses. In this study the agricultural drought risk is defined as the conditional probability of occurring crop-losses under drought conditions. We use the copula theory to estimate joint probability distributions describing the amount of dependence between drought conditions and crop yield anomalies of two major rainfed cereals in the Iberian Peninsula (wheat and barley), in the period 1986–2016. Further conditional probability distributions of the crop yield anomalies under different drought levels are obtained using the Standardized Precipitation Evapotranspiration Index (SPEI) and the satellite derived indices Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). The results suggest that, in general, the joint behaviour of yield anomalies and drought conditions exhibits a dependence between the extreme values, whereas barley exhibits greater probabilities of joint extreme low values of yield and drought indicators. Moreover, while TCI is mainly used in copula models indicating greater probabilities of joint extreme high values of wheat and drought indicators (gumbel models), VCI and SPEI are mainly associated to copula models indicating greater probabilities of joint extreme low values (clayton models). The estimated conditional probabilities of occurrence of crop-loss are illustrated at the province level and suggest that agricultural drought risk increases with drought severity in most of the provinces.

Effect of ICT integration on SC flexibility, agility and company’ performance: the Mexican maquiladora experience

This article reports a structural equation model (SEM) with four latent variables to measure the relationship between information and communication technologies (ICT) integration with supply chain flexibility, supply chain agility, and company’s performance. The SEM integrates six hypotheses with relationships among variables and is validated with 378 responses from manufacturing sector to a questionnaire and partial least squares technique is used to evaluate it and test the hypotheses statistically. A sensitivity analysis is conducted in different scenarios to know conditional probabilities of occurrence of dependent variables, since a scenario has occurred in the independent variable with low and high success level. Findings indicate that ICT integration in supply chain facilitate to monitoring the production process, partners integration and have a direct effect on agility and flexibility for manufacturers, providing an active material’ or subassemblies’ flow among partners with greater visibility and making agile and joint decision-making.

Frequency-specific brain dynamics related to prediction during language comprehension

The brain's remarkable capacity to process spoken language virtually in real time requires fast and efficient information processing machinery. In this study, we investigated how frequency-specific brain dynamics relate to models of probabilistic language prediction during auditory narrative comprehension. We recorded MEG activity while participants were listening to auditory stories in Dutch. Using trigram statistical language models, we estimated for every word in a story its conditional probability of occurrence. On the basis of word probabilities, we computed how unexpected the current word is given its context (word perplexity) and how (un)predictable the current linguistic context is (word entropy). We then evaluated whether source-reconstructed MEG oscillations at different frequency bands are modulated as a function of these language processing metrics. We show that theta-band source dynamics are increased in high relative to low entropy states, likely reflecting lexical computations. Beta-band dynamics are increased in situations of low word entropy and perplexity possibly reflecting maintenance of ongoing cognitive context. These findings lend support to the idea that the brain engages in the active generation and evaluation of predicted language based on the statistical properties of the input signal.

Estimating environmental suitability

AbstractMethods for modeling species’ distributions in nature are typically evaluated empirically with respect to data from observations of species occurrence and, occasionally, absence at surveyed locations. Such models are relatively “theory‐free.” In contrast, theories for explaining species’ distributions draw on concepts like fitness, niche, and environmental suitability. This paper proposes that environmental suitability be defined as the conditional probability of occurrence of a species given the state of the environment at a location. Any quantity that is proportional to this probability is a measure of relative suitability and the support of this probability is the niche. This formulation suggests new methods for presence‐background modeling of species distributions that unify statistical methodology with the conceptual framework of niche theory. One method, the plug‐and‐play approach, is introduced for the first time. Variations on the plug‐and‐play approach were studied with respect to their numerical performance on 106 species from an exhaustively sampled presence–absence survey of vegetation in the Canton of Vaud, Switzerland. Additionally, we looked at the robustness of these methods to the presence of irrelevant information and sample size. Although irrelevant variables eroded the predictive performance of all methods, these methods were found to be both numerically and statistically robust.

The Statistical Severe Convective Risk Assessment Model

Abstract This study introduces a system that objectively assesses severe thunderstorm nowcast probabilities based on hourly mesoscale data across the contiguous United States during the period from 2006 to 2014. Previous studies have evaluated the diagnostic utility of parameters in characterizing severe thunderstorm environments. In contrast, the present study merges cloud-to-ground lightning flash data with both severe thunderstorm report and Storm Prediction Center Mesoscale Analysis system data to create lightning-conditioned prognostic probabilities for numerous parameters, thus incorporating null-severe cases. The resulting dataset and corresponding probabilities are called the Statistical Severe Convective Risk Assessment Model (SSCRAM), which incorporates a sample size of over 3.8 million 40-km grid boxes. A subset of five parameters of SSCRAM is investigated in the present study. This system shows that severe storm probabilities do not vary strongly across the range of values for buoyancy parameters compared to vertical shear parameters. The significant tornado parameter [where “significant” refers to tornadoes producing (Fujita scale) F2/(enhanced Fujita scale) EF2 damage] exhibits considerable skill at identifying downstream tornado events, with higher conditional probabilities of occurrence at larger values, similar to effective storm-relative helicity, both findings being consistent with previous studies. Meanwhile, lifting condensation level heights are associated with conditional probabilities that vary little within an optimal range of values for tornado occurrence, yielding less skill in quantifying tornado potential using this parameter compared to effective storm-relative helicity. The systematic assessment of probabilities using convective environmental information could have applications in present-day operational forecasting duties and the upcoming warn-on-forecast initiatives.

A Review - Design of Area and Power Efficient Digital FIR Filter Based On Faithfully Rounded Truncated 12-Bit Constant

Finite impulse response (FIR) designs are given exploitation the conception of rounded truncated multipliers 12bit. we have a tendency to think about the optimization of bit width without sacrificing the frequency response and output preciseness. A smallest amount comes to of dissimilar pairs or groups of symbols and residues are often used to code a set of coefficients support on their likelihood and conditional probability of occurrence. This ingenious idea permits the notion of entropy to be applied as a quantitative measure to evaluate the coding density of various compositions of symbols towards a set of coefficients. No uniform constant quantization with correct filter order is projected to attenuate total region expenditure. Multiple invariable multiplication/increase during an exceedingly in a very} direct FIR structure is implemented exploitation an improved version of truncated multipliers. Multiple constant multiplication/ accumulation during an exceedingly in a very} synchronized direct FIR structure are implemented using an improved version of truncated multipliers.

Statistical Regularities Attract Attention when Task-Relevant.

Visual attention seems essential for learning the statistical regularities in our environment, a process known as statistical learning. However, how attention is allocated when exploring a novel visual scene whose statistical structure is unknown remains unclear. In order to address this question, we investigated visual attention allocation during a task in which we manipulated the conditional probability of occurrence of colored stimuli, unbeknown to the subjects. Participants were instructed to detect a target colored dot among two dots moving along separate circular paths. We evaluated implicit statistical learning, i.e., the effect of color predictability on reaction times (RTs), and recorded eye position concurrently. Attention allocation was indexed by comparing the Mahalanobis distance between the position, velocity and acceleration of the eyes and the two colored dots. We found that learning the conditional probabilities occurred very early during the course of the experiment as shown by the fact that, starting already from the first block, predictable stimuli were detected with shorter RT than unpredictable ones. In terms of attentional allocation, we found that the predictive stimulus attracted gaze only when it was informative about the occurrence of the target but not when it predicted the occurrence of a task-irrelevant stimulus. This suggests that attention allocation was influenced by regularities only when they were instrumental in performing the task. Moreover, we found that the attentional bias towards task-relevant predictive stimuli occurred at a very early stage of learning, concomitantly with the first effects of learning on RT. In conclusion, these results show that statistical regularities capture visual attention only after a few occurrences, provided these regularities are instrumental to perform the task.

Application of time- and magnitude-predictable model for long-term earthquake prediction in Iran

The regional time- and magnitude-predictable model has been applied successfully in diverse regions of the world to describe the occurrence of main shocks. In the current study, the model has been calibrated against the historical and instrumental catalog of Iranian earthquakes. The Iranian plateau is divided into 15 seismogenic provinces; then, the interevent times for strong main shocks have been determined for each one. The empirical relations reported by Papazachos et al. (Tectonophysics 271:295–323, 1997a) for the Alpine–Himalayan belt (including Iran) were adopted except for the constant terms that were calculated separately for every seismotectonic area. By using the calibrated equations developed for the study area and taking into account the occurrence time and magnitude of the last main shocks in each seismogenic source, the time-dependent conditional probabilities of occurrence P(∆t) of the next main shocks during next 10, 20, 30, 40 and 50 years as well as the magnitude of the expected main shocks (M f) have been estimated. The immediate probability (within next 10 years) of a large main shock is estimated to be high and moderate (>35 %) in all regions except zones 9 (M f = 5.8) and 15 (M f = 6.1). However, it should be noted that the probabilities have been estimated for different M f values in 15 regions. Comparing the model predictions with the actual earthquake occurrence rates shows the good performance of the model for Iranian plateau.

Trends and Variability in Severe Snowstorms East of the Rocky Mountains*

Abstract The 100 most severe snowstorms within each of six climate regions east of the Rocky Mountains were analyzed to understand how the frequency of severe snowstorms is associated with seasonal averages of other variables that may be more readily predicted and projected. In particular, temperature, precipitation, and El Niño/La Niña anomalies from 1901 to 2013 were studied. In the southern United States, anomalously cold seasonal temperatures were found to be more closely linked to severe snowstorm development than in the northern United States. The conditional probability of occurrence of one or more severe snowstorms in seasons that are colder than average is 80% or greater in regions of the southern United States, which was found to be statistically significant, while it is as low as 35% when seasonal temperatures are warmer than average. This compares with unconditional probabilities of 55%–60%. For seasons that are wetter (drier) than average, severe snowstorm frequency is significantly greater (less) in the Northern Plains region. An analysis of the seasonal timing of severe snowstorm occurrence found they are not occurring as late in the season in recent decades in the warmest climate regions when compared to the previous 75 years. Since 1977, the median date of occurrence in the last half of the cold season is six or more days earlier in the Southeast, South, and Ohio Valley regions than earlier in the twentieth century. ENSO conditions also were found to have a strong influence on the occurrence of the top 100 snowstorms in the Northeast and Southeast regions.

Emergence of Information and the Origins of Life a Tentative Physical Model

Life and living systems requires the existence of information. It is no doubt difficult to understand how this information could have spontaneously emerged in the first living cells. In fact spontaneous random association of molecules cannot give birth to information exactly as the random association of letters has a poor probability to generate a word. The idea developed in this paper is that the information required for the building up of the ?primordial cell? does not originate from the association of molecules xi, with p(xi), probability of occurrence, but from the association of xi, molecules possessing conditional probabilities p(xi?yj,yk....). As will be shown in the paper, such a situation implies that molecules of conditional probabilities p(xi?yj,yk....) do not associate randomly but should follow an order defined by the nature of these conditional probabilities. As a conditional information, , is associated with the corresponding conditional probability of occurrence it follows that a sequence of conditional probabilities possesses a global information. Moreover the system can spontaneously generate an information if h(xi?yj,yk....) < h(xi). Hence the global information could have been generated through the ?coupling? of many of these individual mathematical uncertainties. The problem discussed in the present paper is precisely the converse of a transfer of information from a source to a destination in a communication channel, which has been extensively studied but the emergence of new information in a system.

Twierdzenie Bayesa po 250 latach

The author recalls that 250 years ago in the British scientific circles appeared theorem of probability of causes, now called Bayes’ theorem (formulated in 1763 by Thomas Bayes). It refers to the situation where there has been some events and once need to assess the probability of another incident which was the reason, i.e. what is cause and what is the effect of this event. Despite the passing years Bayes’ approach in statistical surveys for the conditional probability of occurrence is constantly confirmed and important.