China
Africa
Explore chapters and articles related to this topic
Employee Turnover Prediction Using Single Voting Model
Published in Pethuru Raj Chelliah, Usha Sakthivel, Nagarajan Susila, Applied Learning Algorithms for Intelligent IoT, 2021
R. Valarmathi, M. Umadevi, T. Sheela
It is a theorem for calculating conditional probability of an event. Consider events A and B. Probability of an event A to occur is P(A) and probability of an event B to occur is P(B). Bayes theorem shows the relationship between the probability of an event A before occurring in the event B. Probability of an event A after getting the event B, P(A/B), is as follows: P(AB)=P(BA).P(A)P(B), where A is hypothesis, B is evidence, P(A/B) is conditional probability of A given B which tells that how often A occurs given that B occurs. It is posterior probability, i.e., probability of the event after the evidence has occurred. P(B/A) is conditional probability of B given A which tells that how often B occurs given that A occurs. P(A) is prior probability, i.e., probability of the event before evidence has occurred.
Probabilistic nonlinear dimensionality reduction through gaussian process latent variable models: An overview
Published in Arun Kumar Sinha, John Pradeep Darsy, Computer-Aided Developments: Electronics and Communication, 2019
A Gaussian process (GP) is a non-parametric statistical model that is a distribution over functions. It treats each observed variable as an independent distribution. A prior probability distribution is an assumption of belief before taking into account of evidence. Observing the output of a continuous function provides information regarding its behaviour around that specific point. For a noiseless model, we can be certain that the input must intersect the point at an observed output, as shown in Figure 2. An observation refines our belief to obtain a posterior. A model with noise can infer that the mapping is nearby the observation, thus treating each observed variable as an independent distribution allows us to take into account of the noise model for prediction.
Modeling and simulation in building automation systems
Published in Jan L.M. Hensen, Roberto Lamberts, Building Performance Simulation for Design and Operation, 2019
Here the Bayesian calibration of the gray box model relied on the extension of a previously developed technique (Neumann et al. 2011). It has benefits over traditional methods because prior knowledge of the system is directly incorporated into the estimation task. The prior probability distribution is updated with any measured data to form the posterior probability distribution, which represents the state of knowledge in any inference task. The inference can essentially be thought of as fitting a joint probability distribution to a measured data set. Specifically, conditional probabilities are related through the product rule to derive Bayes’s theorem and allow consideration of “before data” and “after data.”
Comparison of traffic accident injury severity prediction models with explainable machine learning
Published in Transportation Letters, 2023
Elif Cicek, Murat Akin, Furkan Uysal, ReyhanMerve Topcu Aytas
NBC is based on the Bayesian Theory which is used for probability-based learning approaches. NBC calculates the probability of an event that belongs to a specific class. According to the Bayesian Theory, prior probability of an event can be calculated using the posterior probabilities. Let be a training set consisting of n independent values and one class variable . For classification purposes, our aim to find the probability of an event X is belong to one class, i.e. . Probability that the event x is belong to can be calculated with P( and with the Bayesian theorem such that;
An adaptive defense mechanism to prevent advanced persistent threats
Published in Connection Science, 2021
Yi-xi Xie, Li-xin Ji, Ling-shu Li, Zehua Guo, Thar Baker
Bayes theorem, the basis of the inference algorithm, expresses the relationships between the prior probability and the posterior probability. It indicates the relationships between observable status and unobservable status. The risk reasoning process on DBAG combines the vulnerability exploitation model and defenders’ partially observable detected results. In the course of the inference, uncertain states could be represented by conditional probability tables. The joint probability of the attack through multiple time slots is formulated under the dynamic Bayesian network as follows: in which represents the state of node in time slice . represents the parent nodes of .
A policy knowledge- and reasoning-based method for data-analytic city policymaking
Published in Building Research & Information, 2021
Sun-Young Ihm, Hye-Jin Lee, Eun-Ji Lee, Young-Ho Park
In this experiment, Bayesian inference was used to determine how well the model was classified through the generation and learning of the curve model. Bayesian reasoning is a method that uses the prior probability to calculate the probability of a given event. When certain information has been analysed, the factors explaining the analysis are inferred experientially. If these factors are identified, reasoning is conducted to predict the data values that are yet to be analysed (Stephenson, 2000; Wang & Han, 2016). Figures 8 and 9 present visualizations of the results of creating and training a naïve Bayesian model to determine how well the model performs the classification. The accuracy of the results can be determined through the error rates. For all the analysed values, the accuracy refers to the degree to which the predicted values match the actual values. The evaluation index is effective when the category distribution is balanced. However, the error rate is an index for evaluating the observed values that were not predicted accurately. The accuracy and error rate are given by Equations (2) and (3), respectively. Table 6 lists the variables used in the formulas.