scholarly journals CONDITIONAL PROBABILITY DENSITY AND REGRESSION FUNCTION ESTIMATIONS WITH TRANSFORMATION OF DATA

2020 ◽  
Vol 52 (1) ◽  
pp. 1-25
Author(s):  
Taku Moriyama ◽  
Yoshihiko Maesono
Keyword(s):  
Probability Density ◽  
Conditional Probability ◽  
Regression Function ◽  
Conditional Probability Density

Spiking Statistics of Excitatory Neuron with Feedback

2012 ◽  
Vol 3 (2) ◽  
pp. 1-42
Author(s):  
Alexander Vidybida ◽  
Kseniia Kravchuk
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Conditional Probability ◽  
Delayed Feedback ◽  
Neuronal Firing ◽  
Conditional Probability Density ◽  
Function Type ◽  
Type Singularity ◽  
Poisson Stream ◽  
Firing Statistics

Firing statistics of excitatory binding neuron (BN) is considered. The neuron is driven externally by a Poisson stream. Influence of feedback, which conveys every output impulse to the input with time delay , on the statistics of output spikes is studied. The resulting output stream is not Poissonian, and the authors obtain its inter-spike intervals (ISI) distribution for the case of BN, BN with instantaneous, , and delayed, , feedback. Output statistics of neuron with delayed feedback differs essentially from that found for the case of no feedback as well as from the case of instantaneous feedback. ISI distributions, found for delayed feedback, are characterized with jumps, derivative discontinuities and include -function type singularity. Also, for non-zero refractory time, the authors obtain multiple-ISI conditional probability density and prove, that delayed feedback presence results in non-Markovian statistics of neuronal firing. It is concluded, that delayed feedback presence can radically change neuronal firing statistics.

Semi and Nonparametric Conditional Probability Density, a Case Study of Pedestrian Crashes

2021 ◽  
Vol 15 (1) ◽  
pp. 280-288
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Probability Density ◽  
Conditional Probability ◽  
Traffic Safety ◽  
Logit Model ◽  
Likelihood Function ◽  
Methodological Approach ◽  
Conditional Probability Density ◽  
Nonparametric Method ◽  
Smoothing Parameters ◽  
Pedestrian Crashes

Background: Kernel-based methods have gained popularity as employed model residual’s distribution might not be defined by any classical parametric distribution. Kernel-based method has been extended to estimate conditional densities instead of conditional distributions when data incorporate both discrete and continuous attributes. The method often has been based on smoothing parameters to use optimal values for various attributes. Thus, in case of an explanatory variable being independent of the dependent variable, that attribute would be dropped in the nonparametric method by assigning a large smoothing parameter, giving them uniform distributions so their variances to the model’s variance would be minimal. Objectives: The objective of this study was to identify factors to the severity of pedestrian crashes based on an unbiased method. Especially, this study was conducted to evaluate the applicability of kernel-based techniques of semi- and nonparametric methods on the crash dataset by means of confusion techniques. Methods: In this study, two non- and semi-parametric kernel-based methods were implemented to model the severity of pedestrian crashes. The estimation of the semi-parametric densities is based on the adoptive local smoothing and maximization of the quasi-likelihood function, which is similar somehow to the likelihood of the binary logit model. On the other hand, the nonparametric method is based on the selection of optimal smoothing parameters in estimation of the conditional probability density function to minimize mean integrated squared error (MISE). The performances of those models are evaluated by their prediction power. To have a benchmark for comparison, the standard logistic regression was also employed. Although those methods have been employed in other fields, this is one of the earliest studies that employed those techniques in the context of traffic safety. Results: The results highlighted that the nonparametric kernel-based method outperforms the semi-parametric (single-index model) and the standard logit model based on the confusion matrices. To have a vision about the bandwidth selection method for removal of the irrelevant attributes in nonparametric approach, we added some noisy predictors to the models and a comparison was made. Extensive discussion has been made in the content of this study regarding the methodological approach of the models. Conclusion: To summarize, alcohol and drug involvement, driving on non-level grade, and bad lighting conditions are some of the factors that increase the likelihood of pedestrian crash severity. This is one of the earliest studies that implemented the methods in the context of transportation problems. The nonparametric method is especially recommended to be used in the field of traffic safety when there are uncertainties regarding the importance of predictors as the technique would automatically drop unimportant predictors.

scholarly journals Deep-Based Conditional Probability Density Function Forecasting of Residential Loads

2020 ◽  
Vol 11 (4) ◽  
pp. 3646-3657 ◽  
Author(s):  
Mousa Afrasiabi ◽  
Mohammad Mohammadi ◽  
Mohammad Rastegar ◽  
Lina Stankovic ◽  
Shahabodin Afrasiabi ◽  
...  
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Conditional Probability ◽  
Conditional Probability Density ◽  
Conditional Probability Density Function
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