Bayesian Analysis of Proportions via a Hidden Markov Model

Can, Ceren Eda; Ergun, Gul; Soyer, Refik

doi:10.1007/s11009-022-09971-0

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…Hidden Markov models have been widely applied in other areas of research such as time series, econometrics, finance, biology and psychology [13][14][15][16]. Its application in the area of statistical manpower planning has, however, been scanty, just about those cited in this paper.…”

Section: Ugwuowo and Mccleanmentioning

confidence: 97%

Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach

Ossai

Ezra

Ohanuba

et al. 2022

AJPAS

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We aim at tackling the problem of inadequate specification of a Markov manpower model in this paper, by formulating a procedure for validating the inclusion or non-inclusion of some transition parameters in the model. The mover-stayer principle and its extensions are employed to incorporate hidden classes in the model to achieve more homogeneity and this is compared with the model without the hidden classes, which is more parsimonious, using Likelihood ratio statistic, Akaike Information Criterion and Bayesian Information Criterion. The illustration shows a case of manpower data where, up to a certain level of hidden states, homogeneity is more important than parsimony.

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Section: Ugwuowo and Mccleanmentioning

confidence: 97%

Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach

Ossai

Ezra

Ohanuba

et al. 2022

AJPAS

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“…A Markov chain makes a very strong assumption that if we want to predict the future in the sequence, all that matters is the current state, and the states before the current state have no impact on the future state except via the current state. The Hidden Markov Model (HMM) is based on augmenting the Markov chain [7]. HMM referred to as "hidden" because only the observations themselves are directly visible, not the underlying sequence of states that produced them.…”

Section: Application Of Markov Modelmentioning

confidence: 99%

A question answer chatbot using term frequency-inverse document frequency and Markov chain

Li,

Che,

Yang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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The Natural Language Processing has achieved great success theses years, especially in sentiment analysis, text generation, and Human-Machine Interaction. Since the chat generative pretrained transformer developed by OpenAI in November 2022, more chatbots based on Large Language Models have appeared in our life, bring us a more convenient service. This paper we developed a organization self-use chatbot, using the Markov Model and the Term Frequency-Inverse Document Frequency algorithm. We use Term Frequency-Inverse Document Frequency to vectorize user's question and calculate the text similarity to match answer, use the Markov Model to predict user's intentions when the question is too short. This kind of chatbot is suitable for some organizations, for it can be trained with less corpus and topics, and it also has a good performance on user guidance and user satisfaction.

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“…Assuming that there is a target to identify, Θ represent the possible results for the identifying target, and all the possible results are called hypothesis [26]. Θ is set as an identified framework if the set function m:2 Θ →[0, 1] meet the following requirements:…”

Section: Reliability Functionmentioning

confidence: 99%

“…The recognition result is C1 when meeting the conditions of Formulas ( 24) to (26), where m(Θ) is the uncertainty degree, and ε 1 and ε 2 are set as threshold value.…”

Section: D-s Information Fusionmentioning

confidence: 99%

Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory

et al. 2023

Applied Sciences

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Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative identification between the weld residual stress concentration and the early hidden damage. In order to overcome the bottleneck of quantitative identification of weld defect levels with MMM technology, a modified maximum likelihood estimation (MLE) MMM quantitative identifying model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden crack by comparing with synchronous X-ray detection results. Six MMM characteristic parameters, which are, ΔHp(x), Gxmax, Zxmax, ΔHp(y), Gymax and Zymax, are extracted corresponding to the normal state, the hidden crack state and the macroscopic crack, respectively. The MLE values of the six parameters are obtained by the kernel density functions with optimized bandwidth from the view of mathematical statistics. Furthermore, the modified MLE MMM quantitative identifying model is established based on D–S theory to overcome the partial overlap of MLE values among different defect levels, of which the uncertainty is as low as 0.3%. The verification result from scanning electron microscopy (SEM) is consistent with the prediction of the modified MLE MMM model, which provides a new method for quantitative identification of weld defect levels.

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Bayesian Analysis of Proportions via a Hidden Markov Model

Cited by 8 publications

References 32 publications

Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach

Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach

A question answer chatbot using term frequency-inverse document frequency and Markov chain

Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory

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