estimate parameter
Recently Published Documents


TOTAL DOCUMENTS

18
(FIVE YEARS 7)

H-INDEX

3
(FIVE YEARS 1)

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 125-132
Author(s):  
Thesa Adi Purwanto

The VisualGSCA program uses an incorrect algorithm, which results in scale inconsistencies between observed and latent variables. The observed variable is standardized, while the latent variable is normalized. This affects the calculation of the wrong estimate parameter value and the goodness-fit value of FIT and AFIT becomes inaccurate. This error occurs because the algorithm used is not a pure GSCA algorithm but a reduced GSCA algorithm that ignores the structural model, resulting in an incorrect FIT value. This study aims to prove that the old version of the GSCA program has problems using its algorithm so that it can affect the results of its statistical calculations. The data used in this study are data from previous studies that have been processed with the old version of the GSCA program, so that the results can be compared with the latest version of the GSCA program. The results obtained prove that there are indeed differences in the value of the Loading Factor and FIT, so that research that has been done previously needs to be reanalyzed using the latest program.


2020 ◽  
Vol 83 (1) ◽  
Author(s):  
Tatiana Filatova ◽  
Nikola Popovic ◽  
Ramon Grima

AbstractRecent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. We derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations on gene segments, as well as of total nascent RNA on a gene. We also obtain exact expressions for the first two moments of mature RNA fluctuations and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data.


2020 ◽  
Author(s):  
Jacob D. Marold ◽  
Kevin Sforza ◽  
Kathryn Geiger-Schuller ◽  
Tural Aksel ◽  
Sean Klein ◽  
...  

AbstractA collection of programs is presented to analyze the thermodynamics of folding of linear repeat proteins using a 1D Ising model to determine intrinsic folding and interfacial coupling free energies. Expressions for folding transitions are generated for a series of constructs with different repeat numbers and are globally fitted to transitions for these constructs. These programs are designed to analyze Ising parameters for capped homopolymeric consensus repeat constructs as well as heteropolymeric constructs that contain point substitutions, providing a rigorous framework for analysis of the effects of mutation on intrinsic and directional (i.e., N- versus C-terminal) interfacial coupling free-energies. A bootstrap analysis is provided to estimate parameter uncertainty as well as correlations among fitted parameters. Rigorous statistical analysis is essential for interpreting fits using the complex models required for Ising analysis of repeat proteins, especially heteropolymeric repeat proteins. Programs described here are available at https://github.com/barricklab-at-jhu/Ising_programs.


2020 ◽  
Author(s):  
Tatiana Filatova ◽  
Nikola Popovic ◽  
Ramon Grima

AbstractRecent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. By computational binning of the gene into smaller segments, we derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations in each of these segments, as well as for the total nascent RNA on a gene. We also derive exact expressions for the first two moments of mature RNA fluctuations, and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data.


Author(s):  
Greg Dropkin

AbstractIntroductionThe first reported UK case of COVID-19 occurred on 31 January 2020, and a lockdown of unknown duration began on 24 March. One model to forecast disease spread depends on clinical parameters and transmission rates. Output includes the basic reproduction number R0 and the log growth rate r in the exponential phase.MethodsUK data on reported deaths is used to estimate r. A likelihood for the transmission parameters is defined from a gaussian density for r using the mean and standard error of the estimate. Parameter samples from the Metropolis-Hastings algorithm lead to an estimate and credible interval for R0 and forecasts for severe and critical cases, and deaths during a lockdown.ResultsIn the exponential phase, the UK growth rate for log(deaths) is r = 0.224 with s.e. 0.005. R0 = 5.81 with 90% CI (5.08, 6.98). In a 12 week lockdown from 24 March with transmission parameters reduced to 20% of their previous values, around 63,000 severely ill patients will need hospitalisation by mid June, 37,000 critically ill will need intensive care, whilst over 81,000 are expected to die. Had the lockdown begun on 17 March around 16,500 severe, 9,250 critical cases and 18,500 deaths would be expected by early June. With 10% transmission, severe and critical cases peak in April.DiscussionThe R0 estimate is around twice the value quoted by the UK government. The NHS faces extreme pressures, even if transmission is reduced ten-fold. An earlier lockdown could have saved many lives.


2018 ◽  
Vol 7 (1) ◽  
pp. 103-112
Author(s):  
Gita Alexandra ◽  
Novisita Ratu

AbstrakKemampuan berpikir kritis merupakan elemen penting yang harus dimiliki siswa di sekolah. Namun kenyataannya kemampuan berpikir kritis yang dimiliki siswa saat ini berada pada kategori rendah. Salah satu cara yang dapat dilakukan untuk melatih berpikir kritis adalah dengan membiasakan siswa berlatih soal untuk mengembangkan pemikirannya. Penelitian ini bertujuan untuk mendeskripsikan profil kemampuan berpikir kritis matematis siswa SMP dengan Graded Response Models (GRM). Kriteria berpikir kritis yang dijadikan acuan dalam penelitian ini adalah FRISCO (focus, reason, inference, situation, clarity dan overview). Subjek penelitian adalah 3 orang siswa SMP Pangudi Luhur Salatiga. Ketiga subjek diambil berdasarkan tiga kategori, yaitu siswa dengan kategori kemampuan  tinggi yaitu subjek A, berkemampuan sedang yaitu subjek B dan berkemampuan rendah yaitu subjek C. Hasil estimasi parameter kemampuan berpikir kritis matematis siswa menunjukan bahwa subjek A memenuhi kriteria (focus, reason, clarity dan overview). Subjek B memenuhi kriteria (reason dan clarity). Subjek C memenuhi kriteria (situation). Berdasarkan perhitungan estimasi parameter butir soal dengan GRM, ketiga subjek memiliki kemampuan berpikir kritis rata-rata dengan nilai kemampuan antara 1,00 sampai -1,00. AbstractCritical thinking is an important element which students have in the school. However, the critical thinking in the students nowadays is in the lower level. One strategy that could improve the critical thinking is by giving and practicing the tasks to the students continually to upgrade their knowledge. This research aims to describe the students’ critical thinking of mathematic skill profile in Junior High School with Graded Response Models (GRM). The criterion of critical thinking that becomes the core in this research is FRISCO (Focus, Reason, Inference, Situation, Clarify, and Overview). The subject of the research is three students of Pangudi Luhur Junior High School Salatiga. Those three subjects were taken based on three categories which are A subject (the students who have high intelligence), B subject (the students who have standard intelligence), and C subject (the students who have low intelligence). The estimate parameter result of students’ mathematics critical thinking shows that the A subject fulfilled the criterion (focus, reason, clarity and overview), B subject fulfilled the criterion (reason and clarity), and C subject fulfilld the criterion (situation). Based on the calculation of estimate parameter, the questions and GRM, all of three subjects have an average critical thinking ability with skill score between 1,00 until -1,00. 


2017 ◽  
Vol 23 (3) ◽  
pp. 376
Author(s):  
Herlin Venny Johannes ◽  
Septiadi Padmadisastra ◽  
Bertho Tantular

ABSTRACTThis paper present a study for the number of crime that run into underreporting counts. The purpose of the analysis is to estimate parameter of the model which is the actual number of crime. The model is a mixture of the poisson and the binomial distributions developed by Winkelmann (1996). The parameters of the model are estimated by Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. Determination the convergence of the algorithm using trace plot, autocorrelation plot and ergodic mean plot. In the end, estimator of the parameters of the underreported counts model are the simulation sample mean that calculated from the simulation sample of iteration after burn in period until the last iteration.ABSTRAKPenelitian ini mengkaji permodelan data tingkat kejahatan yang mengalami underreporting counts. Tujuan analisis ini adalah untuk menaksir parameter model yaitu banyaknya jumlah tindak kejahatan yang sebenarnya.  Model yang digunakan adalah hasil penggabungan antara distribusi poisson dan distribusi binomial yang dikembengkan oleh Winkelmann (1996). Penaksiran parameter model dilakukan melalui pendekatan bayes dan simulasi Markov Chain Monte Carlo menggunakan algoritma gibbs sampling. Penentuan konvergensi algoritma akan dilakukan melalui trace plot, autocorrelation plot, dan ergodic mean plot. Taksiran parameter model diperoleh dari rata-rata nilai sampel hasil simulasi yang dihitung dari iterasi setelah burn in period sampai dengan iterasi yang terakhir.


Author(s):  
I. A. Kuznetsov ◽  
A. V. Kuznetsov

In this paper, we first develop a model of axonal transport of tubulin-associated unit (tau) protein. We determine the minimum number of parameters necessary to reproduce published experimental results, reducing the number of parameters from 18 in the full model to eight in the simplified model. We then address the following questions: Is it possible to estimate parameter values for this model using the very limited amount of published experimental data? Furthermore, is it possible to estimate confidence intervals for the determined parameters? The idea that is explored in this paper is based on using bootstrapping. Model parameters were estimated by minimizing the objective function that simulates the discrepancy between the model predictions and experimental data. Residuals were then identified by calculating the differences between the experimental data and model predictions. New, surrogate ‘experimental’ data were generated by randomly resampling residuals. By finding sets of best-fit parameters for a large number of surrogate data the histograms for the model parameters were produced. These histograms were then used to estimate confidence intervals for the model parameters, by using the percentile bootstrap. Once the model was calibrated, we applied it to analysing some features of tau transport that are not accessible to current experimental techniques.


Sign in / Sign up

Export Citation Format

Share Document