• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.21-43
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• 2016

A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.709-717
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• 2010

The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function using conditional probability increment ratio. We compared our function with several association rule ranking functions by some numerical examples. As the result, we knew that our decision function was better than the existing functions. The reasons were that the proposed function of the reference value is not affected by a particular association threshold, and our function had a value between -1 and 1 regardless of the range for three association thresholds. And we knew that the ranking function using conditional probability increment ratio was very well reflected in the difference between association rule measures and the minimum association rule thresholds, respectively.

• Korean Journal of Culture and Social Issue
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• v.24 no.3
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• pp.365-383
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• 2018

In this study, to search for events that cause high levels of PTSD symptoms, traumatic events are classified into 'criterion events' that meet DSM-IV-TR criteria and 'life stresssful events', conditional probability of PTSD was confirmed. From a sample of 1,000 adults residing in South Korea, 998 statistically relevant samples were extracted. Criterion events include cases of 'sexual harassment before age 16', 'sightings of other accidents', 'rape before 16', 'domestic violence before 16', 'disaster', 'traffic accidents', 'other accidents'. Life stressful events appeared to be 'legal arrest or detention(person and family)', 'parental separation or divorce', 'failure or despair causing serious stress', 'extreme conflict with family or frequent quarrels'. Among the demographic characteristics, age, marital status, religion were found to affect PTSD symptoms. The implications and limitations of these results are discussed.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.301-309
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• 2019

A methodology to assess seismic fragility of a nuclear power plant (NPP) using a conditional mean spectrum is proposed as an alternative to using a uniform hazard response spectrum. Rather than the single-scenario conditional mean spectrum, which is the conventional conditional mean spectrum based on a single scenario, a multi-scenario conditional mean spectrum is proposed for the case in which no single scenario is dominant. The multi-scenario conditional mean spectrum is defined as the weighted average of different conditional mean spectra, each one of which corresponds to an individual scenario. The weighting factors for scenarios are obtained from a deaggregation of seismic hazards. As a validation example, a seismic fragility assessment of an NPP containment structure is performed using a uniform hazard response spectrum and different single-scenario conditional mean spectra and multi-scenario conditional mean spectra. In the example, the number of scenarios primarily influences the median capacity of the evaluated structure. Meanwhile, the control frequency, a key parameter of a conditional mean spectrum, plays an important role in reducing logarithmic standard deviation of the corresponding fragility curves and corresponding high confidence of low probability of failure (HCLPF) capacity.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.563-568
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• 1995

It is well-known that the sample mean and the sample variance are jointly sufficient under normality assumption. In this paper a proof of the joint sufficiency is given without using the factorization criterion. It is related to a finite Sanov-type conditional theorem, i.e., the conditional probability density of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are independently and identically distributed (i.i.d.) normal random variables with mean m and variance $\delta^2$, equals that of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are i.i.d. normal random variables with mean $\mu$ and variance $\sigma^2$.

• Korean Journal of Logic
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• v.15 no.2
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• pp.223-250
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• 2012

The reference-class problem is known as a problem that frequentism on the nature of probability is supposed to encounter. Alan H$\acute{a}$jek argues that other theories on the nature of probability also meet this problem inevitably and claims that we can resolve the problem by regarding conditional probabilities as primitive. In this paper I shall present an adequate way of understanding the reference-class problem and its philosophical implications by scrutinizing his argument. H$\acute{a}$jek's claim is to be classified into the following two: (i) probability is relative to its reference class and (ii) what is known as the 'Ratio' analysis of conditional probability is wrong. H$\acute{a}$jek believes that these two are to be closely related but I believe these two should be separated. Moreover, I shall claim that we should accept the former but not the latter. Finally, regarding the identity condition of reference class I shall distinguish the extensional criterion from the non-extensional one. I shall claim that the non-extensional criterion is the right one for the identity condition of reference class by arguing that the reference-class problem should be regarded as an instance of the qua-problem.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.31-35
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• 2000

We shall give upper and lower bounds of the odds ratio of an event by a slight condition of the conditional probability of events.

• Molecules and Cells
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• v.45 no.7
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• pp.444-453
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• 2022

Multivalent macromolecular interactions underlie dynamic regulation of diverse biological processes in ever-changing cellular states. These interactions often involve binding of multiple proteins to a linear lattice including intrinsically disordered proteins and the chromosomal DNA with many repeating recognition motifs. Quantitative understanding of such multivalent interactions on a linear lattice is crucial for exploring their unique regulatory potentials in the cellular processes. In this review, the distinctive molecular features of the linear lattice system are first discussed with a particular focus on the overlapping nature of potential protein binding sites within a lattice. Then, we introduce two general quantitative frameworks, combinatorial and conditional probability models, dealing with the overlap problem and relating the binding parameters to the experimentally measurable properties of the linear lattice-protein interactions. To this end, we present two specific examples where the quantitative models have been applied and further extended to provide biological insights into specific cellular processes. In the first case, the conditional probability model was extended to highlight the significant impact of nonspecific binding of transcription factors to the chromosomal DNA on gene-specific transcriptional activities. The second case presents the recently developed combinatorial models to unravel the complex organization of target protein binding sites within an intrinsically disordered region (IDR) of a nucleoporin. In particular, these models have suggested a unique function of IDRs as a molecular switch coupling distinct cellular processes. The quantitative models reviewed here are envisioned to further advance for dissection and functional studies of more complex systems including phase-separated biomolecular condensates.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.243-247
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• 2014

Let {$X_n$, $n{\geq}1$} be a sequence of i.i.d. random variables with absolutely continuous cumulative distribution function(cdf) F(x) and the corresponding probability density function(pdf) f(x). In this paper, we give characterizations of Pareto and Weibull distribution by considering conditional expectations of record values.