fixed probability
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2021 ◽  
Vol 25 (6) ◽  
pp. 45-52
Author(s):  
A. A. Solodov

The aim of the study is to develop a mathematical model of the trained Markov cognitive system in the presence of discrete training and interfering random stimuli arising at random times at its input. The research method consists in the application of the simplest Markov learning model of Estes with a stochastic matrix with two states, in which the transition probabilities are calculated in accordance with the optimal Neуman-Pearson algorithm for detecting stimuli affecting the system. The paper proposes a model of the random appearance of images at the input of the cognitive system (in terms of learning theory, these are stimuli to which the system reacts). The model assumes an exponential distribution of the system’s response time to stimuli that is widely used to describe intellectual work, while their number is distributed according to the Poisson law. It is assumed that the cognitive system makes a decision about the presence or absence of a stimulus at its input in accordance with the Neуman-Pearson optimality criterion, i.e. maximizes the probability of correct detection of the stimulus with a fixed probability of false detection. The probabilities calculated in this way are accepted as transition probabilities in the stochastic learning matrix of the system. Thus, the following assumptions are accepted in the work, apparently corresponding to the behavior of the system assuming human reactions, i.e. the cognitive system.The images analyzed by the system arise at random moments of time, while the duration of time between neighboring appearances of images is distributed exponentially.The system analyzes the resulting images and makes a decision about the presence or absence of an image at its input in accordance with the optimal Neуman-Pearson algorithm that maximizes the probability of correct identification of the image with a fixed probability of false identification.The system is trainable in the sense that decisions about the presence or absence of an image are made sequentially on a set of identical situations, and the probability of making a decision depends on the previous decision of the system.The new results of the study are analytical expressions for the probabilities of the system staying in each of the possible states, depending on the number of steps of the learning process and the intensities of useful and interfering stimuli at the input of the system. These probabilities are calculated for an interesting case in which the discreteness of the appearance of stimuli in time is clearly manifested and the corresponding graphs are given. Stationary probabilities are also calculated, i.e. for an infinite number of training steps, the probabilities of the system staying in each of the states and the corresponding graph is presented.In conclusion, it is noted that the presented graphs of the behavior of the trained system correspond to an intuitive idea of the reaction of the cognitive system to the appearance of stimuli. Some possible directions of further research on the topic mentioned in the paper are indicated.


Author(s):  
Aseel H. Al-nakash ◽  
◽  
Siraj Q. Mahdi ◽  
Oras A. Al-Ani

Distinguishing the nature of events in Wireless Sensor Networks (WSNs), whether static or dynamic, determines the type of network action. The classification process is accomplished by adjusting the probability for electing the network head. In this paper, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol based on the adaptive clustering technique is adopted with two scenarios. In the first scenario, a homogenous environment with a dynamic event is deployed to continuously send the sensing events during the entire period in order to maintain the communication between the base station and the network. Meanwhile, the second scenario deals with heterogeneous environments for static and dynamic events. The static-heterogeneous event is established through sending the data in a discrete manner, while the continuous-heterogeneous event sends the data in a dynamic event. Simulation results using Matlab 2019b indicate that the throughput and lifetime are probability dependent, where increasing the probability value to 0.2 in homogenous networks leads to increased throughput, as it reaches approximately (14402) packets compared to (12029) packets in the fixed probability scenario. In contrast to the heterogeneous network, the lifetime is increased to reach (2806) rounds compared to fixed probability, which achieves (2160) rounds.


2020 ◽  
Author(s):  
Takuya Yabu

I thought about whether to receive positive or negative emotions from an event from the perspective of human character. Regarding the human character, I define it as a process of selecting one's emotion x so that the received emotion x becomes x=0 with respect to the event X and the reaction of the other party when one's thoughts and reactions occur as the accompanying reactions. Mathematically modeled it, the probability density function of how much to select an emotion has a fixed probability distribution. I also described how to deal with one's character as an application of this model.


Author(s):  
László Losonczi ◽  
Zsolt Páles ◽  
Amr Zakaria

Abstract Given two functions $$f,g:I\rightarrow \mathbb {R}$$ f , g : I → R and a probability measure $$\mu $$ μ on the Borel subsets of [0, 1], the two-variable mean $$M_{f,g;\mu }:I^2\rightarrow I$$ M f , g ; μ : I 2 → I is defined by $$\begin{aligned} M_{f,g;\mu }(x,y) :=\bigg (\frac{f}{g}\bigg )^{-1}\left( \frac{\int _0^1 f\big (tx+(1-t)y\big )d\mu (t)}{\int _0^1 g\big (tx+(1-t)y\big )d\mu (t)}\right) \quad (x,y\in I). \end{aligned}$$ M f , g ; μ ( x , y ) : = ( f g ) - 1 ∫ 0 1 f ( t x + ( 1 - t ) y ) d μ ( t ) ∫ 0 1 g ( t x + ( 1 - t ) y ) d μ ( t ) ( x , y ∈ I ) . This class of means includes quasiarithmetic as well as Cauchy and Bajraktarević means. The aim of this paper is, for a fixed probability measure $$\mu $$ μ , to study their equality problem, i.e., to characterize those pairs of functions (f, g) and (F, G) for which $$\begin{aligned} M_{f,g;\mu }(x,y)=M_{F,G;\mu }(x,y) \quad (x,y\in I) \end{aligned}$$ M f , g ; μ ( x , y ) = M F , G ; μ ( x , y ) ( x , y ∈ I ) holds. Under at most sixth-order differentiability assumptions for the unknown functions f, g and F, G, we obtain several necessary conditions in terms of ordinary differential equations for the solutions of the above equation. For two particular measures, a complete description is obtained. These latter results offer eight equivalent conditions for the equality of Bajraktarević means and of Cauchy means.


2019 ◽  
Author(s):  
Marc Manceau ◽  
Ankit Gupta ◽  
Timothy Vaughan ◽  
Tanja Stadler

AbstractWe consider a homogeneous birth-death process with three different sampling schemes. First, individuals can be sampled through time and included in a reconstructed tree. Second, they can be sampled through time and only recorded as a point ‘occurrence’ along a timeline. Third, extant individuals are sampled and included in the reconstructed tree with a fixed probability. We further consider that sampled individuals can be removed or not from the process, upon sampling, with fixed probability.Given an outcome of the process, composed of the joint observation of a reconstructed phylogenetic tree and a record of occurrences not included in the tree, we derive the conditional probability distribution of the population size any time in the past. We additionally provide an algorithm to simulate ancestral population size trajectories given the observation of a reconstructed tree and occurrences.This distribution can readily be used to perform inferences of the ancestral population size in the field of epidemiology and macroevolution. In epidemiology, these results will pave the way towards jointly considering data from case count studies and reconstructed transmission trees. In macroevolution, it will foster the joint examination of the fossil record and extant taxa to reconstruct past biodiversity.


2019 ◽  
Vol 72 (2) ◽  
pp. 373-391
Author(s):  
Ugo Bindini

AbstractWe consider a multimarginal transport problem with repulsive cost, where the marginals are all equal to a fixed probability $\unicode[STIX]{x1D70C}\in {\mathcal{P}}(\mathbb{R}^{d})$. We prove that, if the concentration of $\unicode[STIX]{x1D70C}$ is less than $1/N$, then the problem has a solution of finite cost. The result is sharp, in the sense that there exists $\unicode[STIX]{x1D70C}$ with concentration $1/N$ for which the cost is infinite.


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