Journal articles: 'Uniform prior'

1

Hartigan, J. A. "Locally uniform prior distributions." Annals of Statistics 24, no. 1 (February 1996): 160–73. http://dx.doi.org/10.1214/aos/1033066204.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Shulman, N., and M. Feder. "The Uniform Distribution as a Universal Prior." IEEE Transactions on Information Theory 50, no. 6 (June 2004): 1356–62. http://dx.doi.org/10.1109/tit.2004.828152.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

van Zwet, Erik. "A default prior for regression coefficients." Statistical Methods in Medical Research 28, no. 12 (December 13, 2018): 3799–807. http://dx.doi.org/10.1177/0962280218817792.

Full text

Abstract:

When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences. We propose that a more suitable default choice is the normal distribution with mean zero and standard deviation equal to the standard error of the M-estimator. We base this recommendation on two arguments. First, we show that this prior is non-informative for inference about the sign of the regression coefficient. Second, we show that this prior agrees well with a meta-analysis of 50 articles from the MEDLINE database.

APA, Harvard, Vancouver, ISO, and other styles

4

Kulawik, S. S., K. W. Bowman, M. Luo, C. D. Rodgers, and L. Jourdain. "Impact of nonlinearity on changing the a priori of trace gas profile estimates from the Tropospheric Emission Spectrometer (TES)." Atmospheric Chemistry and Physics 8, no. 12 (June 20, 2008): 3081–92. http://dx.doi.org/10.5194/acp-8-3081-2008.

Full text

Abstract:

Abstract. Non-linear maximum a posteriori (MAP) estimates of atmospheric profiles from the Tropospheric Emission Spectrometer (TES) contains a priori information that may vary geographically, which is a confounding factor in the analysis and physical interpretation of an ensemble of profiles. One mitigation strategy is to transform profile estimates to a common prior using a linear operation thereby facilitating the interpretation of profile variability. However, this operation is dependent on the assumption of not worse than moderate non-linearity near the solution of the non-linear estimate. The robustness of this assumption is tested by comparing atmospheric retrievals from the Tropospheric Emission Spectrometer processed with a uniform prior with those processed with a variable prior and converted to a uniform prior following the non-linear retrieval. Linearly converting the prior following a non-linear retrieval is shown to have a minor effect on the results as compared to a non-linear retrieval using a uniform prior when compared to the expected total error, with less than 10% of the change in the prior ending up as unbiased fluctuations in the profile estimate results.

APA, Harvard, Vancouver, ISO, and other styles

5

Mukhopadhyay, S., and M. Ghosh. "On the Uniform Approximation of Laplace′s Prior by t-Priors in Location Problems." Journal of Multivariate Analysis 54, no. 2 (August 1995): 284–94. http://dx.doi.org/10.1006/jmva.1995.1057.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Kulawik, S. S., K. W. Bowman, M. Luo, C. D. Rodgers, and L. Jourdain. "Technical Note: Impact of nonlinearity on changing the a priori of trace gas profiles estimates from the Tropospheric Emission Spectrometer (TES)." Atmospheric Chemistry and Physics Discussions 8, no. 1 (January 25, 2008): 1261–89. http://dx.doi.org/10.5194/acpd-8-1261-2008.

Full text

Abstract:

Abstract. Non-linear optimal estimates of atmospheric profiles from the Tropospheric Emission Spectrometer (TES) may contain a priori information that varies geographically, which is a confounding factor in the analysis and physical interpretation of an ensemble of profiles. A common strategy is to transform these profile estimates to a common prior using a linear operation thereby facilitating the interpretation of profile variability. However, this operation is dependent on the assumption of not worse than moderate non-linearity near the solution of the non-linear estimate. We examines the robustness of this assumption when exchanging the prior by comparing atmospheric retrievals from the Tropospheric Emission Spectrometer processed with a uniform prior with those processed with a variable prior and converted to a uniform prior following the non-linear retrieval. We find that linearly converting the prior following a non-linear retrieval is shown to have a minor effect on the results as compared to a non-linear retrieval using a uniform prior when compared to the expected total error, with less than 10% of the change in the prior ending up as unbiased fluctuations in the profile estimate results.

APA, Harvard, Vancouver, ISO, and other styles

7

Tak, H. "Frequency coverage properties of a uniform shrinkage prior distribution." Journal of Statistical Computation and Simulation 87, no. 15 (July 8, 2017): 2929–39. http://dx.doi.org/10.1080/00949655.2017.1349769.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Di, Ruohai, Peng Wang, Chuchao He, and Zhigao Guo. "Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters." Entropy 23, no. 10 (September 30, 2021): 1283. http://dx.doi.org/10.3390/e23101283.

Full text

Abstract:

Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.

APA, Harvard, Vancouver, ISO, and other styles

9

Cunanan, Kristen M., Alexia Iasonos, Ronglai Shen, and Mithat Gönen. "Variance prior specification for a basket trial design using Bayesian hierarchical modeling." Clinical Trials 16, no. 2 (December 7, 2018): 142–53. http://dx.doi.org/10.1177/1740774518812779.

Full text

Abstract:

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. Results: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero [Formula: see text], this can lead to unacceptably high false-positive rates [Formula: see text] in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. Conclusion: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

APA, Harvard, Vancouver, ISO, and other styles

10

Zhu, Hai, Xia Luo, Yanjin Li, Ying Zhu, and Qian Huang. "Comparing the efficiency and robustness of state-of-the-art experimental designs for stated choice modeling: A simulation analysis." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769189. http://dx.doi.org/10.1177/1687814017691894.

Full text

Abstract:

Among the ways to construct experimental designs having been proposed, orthogonal design, uniform design, and D-efficient design are state-of-the-art methods. This article provides detailed comparisons on the efficiency and robustness among these methods with three case studies in multinomial logit and mixed multinomial logit models. ND-error values and the departures of D-errors corresponding to misspecification of prior information are used as measurements of design efficiency and design robustness, respectively. Design methods are described, and designs with various numbers of runs are constructed. The results indicate that (a) when parameter priors are available, D-efficient design method outperforms the other two methods, in terms of design efficiency, while uniform design and orthogonal design methods are comparable with each other; (b) there will be efficiency loss when D-efficient design that constructed for specific model is implemented in other ones; (c) all three methods have comparable robustness against misspecifications in parameter prior values; however, the effect of misspecification in prior distribution is massive when D-efficient design is used in mixed multinomial logit model; and (d) when parameter priors are unknown, uniform design is suggested to be used in the construction of experimental designs.

APA, Harvard, Vancouver, ISO, and other styles

11

Iliasu, G. B., A. A. Kogo, and M. K. Yakubu. "Optimization of mechanical properties of chitosan/phenol formaldehyde composite." Bayero Journal of Pure and Applied Sciences 11, no. 1 (November 5, 2018): 229–35. http://dx.doi.org/10.4314/bajopas.v11i1.39.

Full text

Abstract:

The Frechet distribution which has a scale and shape parameters, has been found to have wide application in modelling extreme events such as radioactive emission, flood, rainfall, seismic analysis, wind speed, etc. In this research paper, the Bayesian analysis of scale parameter of Frechet distribution was considered. It is necessary to know the best combination of prior distribution and loss function for the parameter estimation. Posterior distribution was derived by uniform and Jeffrey’s prior under the square error, Precautionary, Quadratic and Weighted balance loss function. Bayes estimation and their corresponding risk was obtained by the above stated priors and loss function. Monte Carlo simulations was conducted to compare the performance of the estimators. It is evident that weighted balance loss function when used with uniform prior provides the least posterior risk.Keywords: Frechet Distribution, Non-Informative Prior, Bayesian Estimation, Loss Functions, Monte Carlo Simulations

APA, Harvard, Vancouver, ISO, and other styles

12

Baluev, Roman V. "Comparing the frequentist and Bayesian periodic signal detection: rates of statistical mistakes and sensitivity to priors." Monthly Notices of the Royal Astronomical Society 512, no. 4 (March 21, 2022): 5520–34. http://dx.doi.org/10.1093/mnras/stac762.

Full text

Abstract:

ABSTRACT We perform extensive Monte Carlo simulations to systematically compare the frequentist and Bayesian treatments of the Lomb–Scargle periodogram. The goal is to investigate whether the Bayesian period search is advantageous over the frequentist one in terms of the detection efficiency, how much if yes, and how sensitive it is regarding the choice of the priors, in particular in case of a misspecified prior (whenever the adopted prior does not match the actual distribution of physical objects). We find that the Bayesian and frequentist analyses always offer nearly identical detection efficiency in terms of their trade-off between type-I and type-II mistakes. Bayesian detection may reveal a formal advantage if the frequency prior is non-uniform, but this results in only ∼1 per cent extra detected signals. In case if the prior was misspecified (adopting non-uniform one over the actual uniform) this may turn into an opposite advantage of the frequentist analysis. Finally, we revealed that Bayes factor of this task appears rather overconservative if used without a calibration against type-I mistakes (false positives), thereby necessitating such a calibration in practice.

APA, Harvard, Vancouver, ISO, and other styles

13

Lavenda, B. H. "Derivation of the Prior Distribution in Bayesian Analysis from the Principle of Statistical Equivalence." Open Systems & Information Dynamics 08, no. 02 (June 2001): 103–14. http://dx.doi.org/10.1023/a:1011910512406.

Full text

Abstract:

The distinction between uniform and logarithmic uniform prior distributions is made in terms of the principle of statistical equivalence, consisting of two statistically equivalent experiments, where the variable and parameter of the distribution exchange their roles. The two choices of the prior correspond to two terms in the drift of a diffusion process, and the condition for a stationary solution eliminates the choice of the uniform prior. Parameter randomization gives rise to a new distribution where the parameter of the original distribution is replaced by a ‘hitting point’ value of the variate.

APA, Harvard, Vancouver, ISO, and other styles

14

Kaur, Kamaljit, Sangeeta Arora, and Kalpana K. Mahajan. "Bayesian Estimation of Inequality and Poverty Indices in Case of Pareto Distribution Using Different Priors under LINEX Loss Function." Advances in Statistics 2015 (January 29, 2015): 1–10. http://dx.doi.org/10.1155/2015/964824.

Full text

Abstract:

Bayesian estimators of Gini index and a Poverty measure are obtained in case of Pareto distribution under censored and complete setup. The said estimators are obtained using two noninformative priors, namely, uniform prior and Jeffreys’ prior, and one conjugate prior under the assumption of Linear Exponential (LINEX) loss function. Using simulation techniques, the relative efficiency of proposed estimators using different priors and loss functions is obtained. The performances of the proposed estimators have been compared on the basis of their simulated risks obtained under LINEX loss function.

APA, Harvard, Vancouver, ISO, and other styles

15

Yani, Resti Nanda, Ferra Yanuar, and Hazmira Yozza. "INFERENSI BAYESIAN UNTUK 2 DARI DISTRIBUSI NORMAL DENGAN BERBAGAI DISTRIBUSI PRIOR." Jurnal Matematika UNAND 7, no. 2 (May 1, 2018): 132. http://dx.doi.org/10.25077/jmu.7.2.132-139.2018.

Full text

Abstract:

Abstrak. Pada penelitian ini dilakukan pendugaan parameter variansi (2) dari dis-tribusi Normal dengan mean () diketahui. Pendugaan parameter variansi (2) terse-but dilakukan secara analitik dengan menggunakan distribusi Invers Gamma sebagaiprior konjugat, metode Jerey sebagai prior non-informatif dan distribusi Uniform se-bagai prior non-konjugat. Pada penelitian ini kriteria evaluasi penduga yang digunakanadalah MSE dan sifat tak bias. Berdasarkan studi analitik diperoleh bahwa distribusiInvers Gamma sebagai prior konjugat merupakan prior terbaik diantara dua distribusiprior lainnya.Kata Kunci: Inferensi statistika, metode Bayes, distribusi prior, fungsi likelihood, dis-tribusi Normal, Invers Gamma, metode Jerey, distribusi Uniform

APA, Harvard, Vancouver, ISO, and other styles

16

Gu, Ziqi, Zongqian Zhan, Qiangqiang Yuan, and Li Yan. "Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network." Remote Sensing 11, no. 24 (December 13, 2019): 3008. http://dx.doi.org/10.3390/rs11243008.

Full text

Abstract:

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

17

Negrín-Hernández, Miguel-Angel, María Martel-Escobar, and Francisco-José Vázquez-Polo. "Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models." International Journal of Environmental Research and Public Health 18, no. 2 (January 19, 2021): 809. http://dx.doi.org/10.3390/ijerph18020809.

Full text

Abstract:

In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples and then determining the posterior probability of the cluster models through model selection. The meta-parameter is then estimated using Bayesian model averaging techniques. Although an objective Bayesian meta-analysis is proposed for each type of heterogeneity, we concentrate the attention of this paper on priors over the models. We consider four alternative priors which are motivated by reasonable but different assumptions. A frequentist validation with simulated data has been carried out to analyze the properties of each prior distribution for a set of different number of studies and sample sizes. The results show the importance of choosing an adequate model prior as the posterior probabilities for the models are very sensitive to it. The hierarchical Poisson prior and the hierarchical uniform prior show a good performance when the real model is the homogeneity, or when the sample sizes are high enough. However, the uniform prior can detect the true model when it is an intermediate model (neither homogeneity nor heterogeneity) even for small sample sizes and few studies. An illustrative example with real data is also given, showing the sensitivity of the estimation of the meta-parameter to the model prior.

APA, Harvard, Vancouver, ISO, and other styles

18

Chen, Hsiang-Chun, and Thomas E. Wehrly. "Approximate uniform shrinkage prior for a multivariate generalized linear mixed model." Journal of Multivariate Analysis 145 (March 2016): 148–61. http://dx.doi.org/10.1016/j.jmva.2015.12.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Link, William A. "A cautionary note on the discrete uniform prior for the binomialN." Ecology 94, no. 10 (October 2013): 2173–79. http://dx.doi.org/10.1890/13-0176.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Wu, Yahong, Jieying Zheng, Wanru Song, and Feng Liu. "Low light image enhancement based on non-uniform illumination prior model." IET Image Processing 13, no. 13 (November 14, 2019): 2448–56. http://dx.doi.org/10.1049/iet-ipr.2018.6208.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

YANUAR, Ferra, and Cici Saputri. "Bayesian inference for Pareto distribution with prior conjugate and prior non conjugate." Jurnal Matematika, Statistika dan Komputasi 16, no. 3 (April 28, 2020): 382. http://dx.doi.org/10.20956/jmsk.v16i3.8019.

Full text

Abstract:

The purpose of this study is to determine the best estimator for estimating the shape parameters of the Pareto distribution with the known scale parameter. Estimation of these parameters is done by using the Gamma distribution as the prior distribution of the conjugate and the Uniform distribution as the non-conjugate prior distribution. A comparison of the two prior distributions is done through simulation studies with various sample sizes. The best estimator net is a method that produces the smallest posterior variance, absolute bias, and Bayes confidence interval. This study proves that the Bayes estimator by using the prior conjugate distribution produces all indicators of the goodness of the model with a smaller value than the non-conjugate prior distribution. Thus it can be concluded that the estimator with prior conjugate will produce a better predictive value than prior non-conjugate.

APA, Harvard, Vancouver, ISO, and other styles

22

Li, Guangming, and Like An. "Comparison of Prior Setting Methods for Multilevel Model Effect Estimation Based on Small Sample Imbalanced Nested Data in Bayesian Framework." Computational Intelligence and Neuroscience 2022 (November 14, 2022): 1–18. http://dx.doi.org/10.1155/2022/2726602.

Full text

Abstract:

In the fields of education and psychology, nested data with small samples and imbalances are very common. Bauer et al. (2008) first proposed adjusting the traditional multilevel model to analyze the small sample imbalanced nested data (SSIND). In terms of parameter estimation, the Bayesian method shows the possibility of providing unbiased estimation when the sample size is small. This study proposes that the Bayesian method should be used to analyze the SSIND. This study explores the performance of different treatment effects and nesting effects estimation methods in the multilevel model based on the Bayesian method that performs well in the case of small samples, to provide an appropriate and scientific method reference for the subsequent analysis of the model. Two prior setting methods are compared for multilevel model effect estimation based on a small sample of imbalanced nested data in the Bayesian framework. Two prior setting methods are gamma prior setting method and uniform prior setting method. The research results show that when the treatment condition ICC is small (0.05), the bias and RMSE values of the parameter estimation by the gamma prior setting method are larger and the performance is unstable, while the bias and RMSE values of the parameter estimation by the uniform prior setting method are smaller and the performance is relatively stable, so the uniform prior setting method is recommended; when the treatment condition ICC is large (0.15), the bias and RMSE values of the parameter estimation by the uniform prior setting method are larger and the performance is unstable, while the bias and RMSE values of the parameter estimation by the gamma prior setting method are smaller and the performance is relatively stable, so the gamma prior setting method is recommended; when the treatment condition ICC is between 0.05 and 0.15, both prior setting methods have similar effects. Furthermore, when the number of treatment groups is small (8), the gamma prior setting method is recommended; when the number of treatment groups is large (16), the uniform prior setting method is recommended; when the number of treatment groups is between 8 and 16, both prior setting methods have similar effects. Summarily, when we choose which prior setting method to use for the SSIND, we must consider the interaction between the ICC and the number of treatment groups.

APA, Harvard, Vancouver, ISO, and other styles

23

Rama, Taraka. "Three tree priors and five datasets." Language Dynamics and Change 8, no. 2 (October 1, 2018): 182–218. http://dx.doi.org/10.1163/22105832-00802005.

Full text

Abstract:

Abstract The age of the root of the Indo-European language family has received much attention since the application of Bayesian phylogenetic methods by Gray and Atkinson (2003). With the application of new models, the root age of the Indo-European family has tended to decrease from an age that supported the Anatolian origin hypothesis to an age that supports the Steppe origin hypothesis (Chang et al., 2015). However, none of the published work in Indo-European phylogenetics has studied the effect of tree priors on phylogenetic analyses of the Indo-European family. In this paper, I intend to fill this gap by exploring the effect of tree priors on different aspects of the Indo-European family’s phylogenetic inference. I apply three tree priors—Uniform, Fossilized Birth-Death (FBD), and Coalescent—to five publicly available datasets of the Indo-European language family. I evaluate the posterior distribution of the trees from the Bayesian analysis using Bayes Factor, and find that there is support for the Steppe origin hypothesis in the case of two tree priors. I report the median and 95 % highest posterior density (HPD) interval of the root ages for all three tree priors. A model comparison suggests that either the Uniform prior or the FBD prior is more suitable than the Coalescent prior to the datasets belonging to the Indo-European language family.

APA, Harvard, Vancouver, ISO, and other styles

24

Khooriphan, Wansiri, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for the Ratio of Variances of Delta-Gamma Distributions with Applications." Axioms 11, no. 12 (November 30, 2022): 689. http://dx.doi.org/10.3390/axioms11120689.

Full text

Abstract:

Since rainfall data often contain zero observations, the ratio of the variances of delta-gamma distributions can be used to compare the rainfall dispersion between two rainfall datasets. To this end, we constructed the confidence interval for the ratio of the variances of two delta-gamma distributions by using the fiducial quantity method, Bayesian credible intervals based on the Jeffreys, uniform, or normal-gamma-beta priors, and highest posterior density (HPD) intervals based on the Jeffreys, uniform, or normal-gamma-beta priors. The performances of the proposed confidence interval methods were evaluated in terms of their coverage probabilities and average lengths via Monte Carlo simulation. Our findings show that the HPD intervals based on Jeffreys prior and the normal-gamma-beta prior are both suitable for datasets with a small and large probability of containing zeros, respectively. Rainfall data from Phrae province, Thailand, are used to illustrate the practicability of the proposed methods with real data.

APA, Harvard, Vancouver, ISO, and other styles

25

Laidat, Laura Erlinda, Keristina Br Ginting, and Ganesha Lapenangga Putra. "ESTIMASI PARAMETER DISTRIBUSI BINOMIAL NEGATIF MENGGUNAKAN METODE INFERENSI BAYESIAN." Jurnal Diferensial 4, no. 1 (April 27, 2022): 35–43. http://dx.doi.org/10.35508/jd.v4i1.6130.

Full text

Abstract:

Estimasi parameter merupakan salah satu bentuk dari statistik inferensial. Estimasi parameter terdiri atas estimasi parameter titik dan estimasi parameter interval. Pada penelitian ini akan dilakukan estimasi parameter titik dan interval dari distribusi Binomial Negatif dengan metode Bayes. Metode Bayes dalam penelitian ini memanfaatkan distribusi Beta selaku prior konjugat, distribusi Uniform selaku prior non-konjugat, dan metode Jeffrey selaku prior non-informatif. Untuk mengevaluasi penduga terbaik, metode yang digunkan ialah dengan melihat nilai yang terkecil dari varian posterior dan lebar credible interval bayes. Dalam studi simulasi menggunakan pemrograman R, diperoleh penduga terbaik ialah prior konjugat Beta, sebab memiliki nilai varian posterior yang terkecil dan lebar credible interval Bayes yang terkecil dibandingkan dengan prior non-konjugat Uniform dan prior non-informatif Jeffrey.

APA, Harvard, Vancouver, ISO, and other styles

26

Shchukina, Alexandra, Magdalena Kaźmierczak, Paweł Kasprzak, Matthew Davy, Geoffrey R. Akien, Craig P. Butts, and Krzysztof Kazimierczuk. "Accelerated acquisition in pure-shift spectra based on prior knowledge from 1H NMR." Chemical Communications 55, no. 64 (2019): 9563–66. http://dx.doi.org/10.1039/c9cc05222d.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Link, William A. "A cautionary note on the discrete uniform prior for the binomialN: reply." Ecology 95, no. 9 (September 2014): 2677–79. http://dx.doi.org/10.1890/14-0857.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Park, Hye Yoon, Xiangyun Qiu, Elizabeth Rhoades, Jonas Korlach, Lisa W. Kwok, Warren R. Zipfel, Watt W. Webb, and Lois Pollack. "Achieving Uniform Mixing in a Microfluidic Device: Hydrodynamic Focusing Prior to Mixing." Analytical Chemistry 78, no. 13 (July 2006): 4465–73. http://dx.doi.org/10.1021/ac060572n.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Mowlaee, Pejman, Johannes Stahl, and Josef Kulmer. "Iterative joint MAP single-channel speech enhancement given non-uniform phase prior." Speech Communication 86 (February 2017): 85–96. http://dx.doi.org/10.1016/j.specom.2016.11.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Villa, Cristiano, and Stephen G. Walker. "A cautionary note on the discrete uniform prior for the binomialN: comment." Ecology 95, no. 9 (September 2014): 2674–77. http://dx.doi.org/10.1890/14-0333.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Al-Jabban, Wathiq, Jan Laue, Sven Knutsson, and Nadhir Al-Ansari. "Effect of Disintegration Times of the Homogeneity of Soil prior to Treatment." Applied Sciences 9, no. 22 (November 9, 2019): 4791. http://dx.doi.org/10.3390/app9224791.

Full text

Abstract:

This paper presents an experimental study to investigate the effect of various disintegration times on the homogeneity of pre-treated natural soil before mixing with cementitious binders. Various disintegration times were applied, ranging from 10 s to 120 s. Four different soils were used with different characteristics from high, medium and low plasticity properties. Visual and sieving assessment were used to evaluate the best disintegration times to allow for a uniform distribution of water content and small-sized particles that would produce a uniform distribution of the binder around the soil particles. Results showed that a proper mixing time to homogenize and disintegrate the soil prior to treatment depended on several factors: soil type, water content and plasticity properties. For high plasticity soil, the disintegration time should be kept as short as possible. Increasing the disintegration time ha negative effects on the uniformity of distribution of the binder around soil particles. The homogenizing and disintegration time were less important for low plasticity soils with low water content than for medium to high plasticity soils. The findings could assist various construction projects that deal with soil improvement through preparation of soil before adding a cementitious binder to ensure uniformity of distribution of the binder around soil particles and obtain uniform soil–binder mixtures.

APA, Harvard, Vancouver, ISO, and other styles

32

Jiang, Ruichao, Javad Tavakoli, and Yiqiang Zhao. "Weyl Prior and Bayesian Statistics." Entropy 22, no. 4 (April 20, 2020): 467. http://dx.doi.org/10.3390/e22040467.

Full text

Abstract:

When using Bayesian inference, one needs to choose a prior distribution for parameters. The well-known Jeffreys prior is based on the Riemann metric tensor on a statistical manifold. Takeuchi and Amari defined the α -parallel prior, which generalized the Jeffreys prior by exploiting a higher-order geometric object, known as a Chentsov–Amari tensor. In this paper, we propose a new prior based on the Weyl structure on a statistical manifold. It turns out that our prior is a special case of the α -parallel prior with the parameter α equaling − n , where n is the dimension of the underlying statistical manifold and the minus sign is a result of conventions used in the definition of α -connections. This makes the choice for the parameter α more canonical. We calculated the Weyl prior for univariate Gaussian and multivariate Gaussian distribution. The Weyl prior of the univariate Gaussian turns out to be the uniform prior.

APA, Harvard, Vancouver, ISO, and other styles

33

YUZAN, SUMINDANG, FERRA YANUAR, and DODI DEVIANTO. "PENDUGAAN PARAMETER DARI DISTRIBUSI GEOMETRIK DENGAN METODE BAYES." Jurnal Matematika UNAND 8, no. 3 (December 13, 2019): 85. http://dx.doi.org/10.25077/jmu.8.3.85-92.2019.

Full text

Abstract:

Pendugaan parameter adalah prosedur yang dilakukan untuk menduga parameter populasi dimana parameter tersebut merupakan sebarang nilai yang menjelaskan ciri dari suatu populasi. Pendugaan paramater terdiri dari pendugaan titik dan pendugaan selang. Pendugaan parameter untuk parameter θ dari distribusi Geometrik menggunakan metode Bayes dengan distribusi prior yang digunakan adalah distribusi Beta(α,β) sebagai distribusi prior konjugat, distribusi Uniform(0,1) sebagai distribusi prior non-konjugat dan distribusi prior Jeffrey sebagai distribusi prior noninformatif. Metode evaluasi yang digunakan untuk mengevaluasi penduga terbaik adalah berdasarkan nilai varian posterior dan lebar credible interval Bayes yang terkecil. Dalam studi simulasi yang dilakukan menunjukkan bahwa distribusi Beta(α,β) menghasilkan nilai dugaan parameter yang lebih baik dari pada distribusi Uniform dan distribusi prior Jeffrey karena menghasilkan nilai varian posterior dan lebar credible interval Bayes yang terkecil.Kata Kunci: Distribusi Beta, Distribusi Geometrik, Metode Bayes.

APA, Harvard, Vancouver, ISO, and other styles

34

Chen, Sheng Yong, Qiu Guan, Lan Lan Li, Wei Huang, and Li Yong Qian. "Non-Uniform Simplification of Point Clouds." Advanced Materials Research 311-313 (August 2011): 1806–9. http://dx.doi.org/10.4028/www.scientific.net/amr.311-313.1806.

Full text

Abstract:

For three-dimensional mechanical design and reverse engineering in manufacturing, point clouds are obtained from some scanners before they are used to generate geometrical shapes in a design. However, original point clouds are poor in quality because of noise, incomplete, and non-uniform data samples. Simplification is an important step to generate a good result prior to polygonal meshes. Usually we cannot obtain uniform points using traditional cloud simplification methods. This paper proposes a new method for non-uniform points cloud simplification (NUPCS), which is based on affinity propagation clustering. Experiments are carried out for some data sets and results show that our proposed method can deliver good simplification performances.

APA, Harvard, Vancouver, ISO, and other styles

35

Junnumtuam, Sunisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "A Zero-and-One Inflated Cosine Geometric Distribution and Its Application." Mathematics 10, no. 21 (October 28, 2022): 4012. http://dx.doi.org/10.3390/math10214012.

Full text

Abstract:

Count data containing both excess zeros and ones occur in many fields, and the zero-and-one inflated distribution is suitable for analyzing them. Herein, we construct confidence intervals (CIs) for the parameters of the zero-and-one inflated cosine geometric (ZOICG) distribution constructed by using five methods: a Wald CI based on the maximum likelihood estimate, equal-tailed Bayesian CIs based on the uniform or Jeffreys prior, and the highest posterior density intervals based on the uniform or Jeffreys prior. Their efficiencies were compared in terms of their coverage probabilities and average lengths via a simulation study. The results show that the highest posterior density intervals based on the uniform prior performed the best in most cases. The number of new daily COVID-19-related deaths in Luxembourg in 2020 involving data with a high proportion of zeros and ones were analyzed. It was found that the ZOICG model was appropriate for this scenario.

APA, Harvard, Vancouver, ISO, and other styles

36

Alsing, Justin, and Will Handley. "Nested sampling with any prior you like." Monthly Notices of the Royal Astronomical Society: Letters 505, no. 1 (June 14, 2021): L95—L99. http://dx.doi.org/10.1093/mnrasl/slab057.

Full text

Abstract:

ABSTRACT Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for model comparison. One technical obstacle to using nested sampling in practice is the requirement (for most common implementations) that prior distributions be provided in the form of transformations from the unit hyper-cube to the target prior density. For many applications – particularly when using the posterior from one experiment as the prior for another – such a transformation is not readily available. In this letter, we show that parametric bijectors trained on samples from a desired prior density provide a general purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors. We demonstrate the use of trained bijectors in conjunction with nested sampling on a number of examples from cosmology.

APA, Harvard, Vancouver, ISO, and other styles

37

Wang, Fei, Wenfeng Lu, Chenguang Shi, and Jianjiang Zhou. "Prior knowledge‐based statistical estimation of linear false tracks in uniform distributed clutter." IET Radar, Sonar & Navigation 15, no. 10 (June 3, 2021): 1237–46. http://dx.doi.org/10.1049/rsn2.12107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Hill, Roger M. "Applying Bayesian methodology with a uniform prior to the single period inventory model." European Journal of Operational Research 98, no. 3 (May 1997): 555–62. http://dx.doi.org/10.1016/s0377-2217(96)00226-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Noumeir, Rita, Guy E. Mailloux, and Raymond Lemieux. "An expectation maximization reconstruction algorithm for emission tomography with non-uniform entropy prior." International Journal of Bio-Medical Computing 39, no. 3 (June 1995): 299–310. http://dx.doi.org/10.1016/0020-7101(95)01111-q.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Abdul Majid, Muhammad Hilmi, and Kamarulzaman Ibrahim. "On Bayesian approach to composite Pareto models." PLOS ONE 16, no. 9 (September 23, 2021): e0257762. http://dx.doi.org/10.1371/journal.pone.0257762.

Full text

Abstract:

In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.

APA, Harvard, Vancouver, ISO, and other styles

41

Rahmawati, Asri, and Retno Budiarti. "Perbandingan Estimasi Parameter Metode Bayesian Self dengan Prior Vague dan Uniform Pada Model Survival Berdistribusi Rayleigh." Jurnal Indonesia Sosial Sains 2, no. 3 (March 21, 2021): 351–59. http://dx.doi.org/10.36418/jiss.v2i3.209.

Full text

Abstract:

Analisis survival merupakan analisis yang digunakan untuk menganalisis data kelangsungan waktu hidup (survival time). Waktu survival (survival time) merupakan salah satu penelitian yang digunakan untuk menghitung waktu dari munculnya gejala sampai dengan munculnya kejadian. Dalam analisis survival dikenal istilah data survival yaitu data yang menunjukkan waktu suatu individu dapat bertahan hingga terjadinya suatu kejadian. Penelitian ini akan membahas perbandingan estimasi parameter model survival berdistribusi Rayleigh dengan Metode Bayesian SELF menggunakan prior Vague dan Uniform. Proses estimasi parameter memerlukan informasi dari fungsi likelihood dan distribusi prior yang kemudian akan membentuk distribusi posterior. Setelah diperoleh estimator pada metode Bayesian SELF, selanjutnya diterapkan pada data program transplantasi jantung yang dilakukan Stanford dari Oktober 1967 sampai Februari 1980. Berdasarkan dari nilai MSE pada penelitian ini, diperoleh metode Bayesian SELF dengan prior vague lebih baik dari metode Bayesian SELF dengan Prior uniform.

APA, Harvard, Vancouver, ISO, and other styles

42

Noor, Farzana, Saadia Masood, Mehwish Zaman, Maryam Siddiqa, Raja Asif Wagan, Imran Ullah Khan, and Ahthasham Sajid. "Bayesian Analysis of Inverted Kumaraswamy Mixture Model with Application to Burning Velocity of Chemicals." Mathematical Problems in Engineering 2021 (May 18, 2021): 1–18. http://dx.doi.org/10.1155/2021/5569652.

Full text

Abstract:

Burning velocity of different chemicals is estimated using a model from mixed population considering inverted Kumaraswamy (IKum) distribution for component parts. Two estimation techniques maximum likelihood estimation (MLE) and Bayesian analysis are applied for estimation purposes. BEs of a mixture model are obtained using gamma, inverse beta prior, and uniform prior distribution with two loss functions. Hyperparameters are determined through the empirical Bayesian method. An extensive simulation study is also a part of the study which is used to foresee the characteristics of the presented model. Application of the IKum mixture model is presented through a real dataset. We observed from the results that Linex loss performed better than squared error loss as it resulted in lower risks. And similarly gamma prior is preferred over other priors.

APA, Harvard, Vancouver, ISO, and other styles

43

Li, Hao, and Enze Zhang. "A Coupled Sampling Design for Parameter Estimation in Microalgae Growth Experiment: Maximizing the Benefits of Uniform and Non-Uniform Sampling." Water 13, no. 21 (October 24, 2021): 2996. http://dx.doi.org/10.3390/w13212996.

Full text

Abstract:

As an important primary producer in aquatic ecosystems, the various parameters within the mathematical models are used to describe the growth of microalgae and need to be estimated by carefully designed experiments. Non-uniform sampling has proved to generate a deliberately optimized sampling temporal schedule that can benefit parameter estimation. However, the current non-uniform sampling method depends on prior knowledge of the nominal values of the model parameters. It also largely ignores the uncertainty associated with the nominal values, thus inducing unacceptable parameter estimates. This study focuses on the uncertainty problem and describes a new sampling design that couples the traditional uniform and non-uniform sampling schedules to benefit from the merits of both methods. Based on D-optimal design, we first derive the non-uniform optimal sampling points by maximizing the determinant of the Fisher information matrix. Then the confidence interval around the non-uniform sampling points is determined by Monte Carlo simulations based on the prior knowledge of parameter distribution. Finally, we wrap the non-uniform sampling points with the uniform sampling points within the confidence interval to obtain the ultimate optimal experimental design. Scenedesmus obliquus, whose growth curve follows a four-parameter model, was used as a case study. Compared with the traditional sampling design, the simulation results show that our proposed coupled sampling schedule can partly eliminate the uncertainty in parameter estimates caused by fixed systematic errors in observations. Our coupled sampling can also retain some advantages belonging to non-uniform sampling, in exploiting information maximization and managing the cost of sampling.

APA, Harvard, Vancouver, ISO, and other styles

44

Scales, John A., and Luis Tenorio. "Prior information and uncertainty in inverse problems." GEOPHYSICS 66, no. 2 (March 2001): 389–97. http://dx.doi.org/10.1190/1.1444930.

Full text

Abstract:

Solving any inverse problem requires understanding the uncertainties in the data to know what it means to fit the data. We also need methods to incorporate data‐independent prior information to eliminate unreasonable models that fit the data. Both of these issues involve subtle choices that may significantly influence the results of inverse calculations. The specification of prior information is especially controversial. How does one quantify information? What does it mean to know something about a parameter a priori? In this tutorial we discuss Bayesian and frequentist methodologies that can be used to incorporate information into inverse calculations. In particular we show that apparently conservative Bayesian choices, such as representing interval constraints by uniform probabilities (as is commonly done when using genetic algorithms, for example) may lead to artificially small uncertainties. We also describe tools from statistical decision theory that can be used to characterize the performance of inversion algorithms.

APA, Harvard, Vancouver, ISO, and other styles

45

Johnson, Robert W. "Fitting a sum of exponentials to lattice correlation functions using a non-uniform prior." European Physical Journal C 70, no. 1-2 (September 30, 2010): 233–41. http://dx.doi.org/10.1140/epjc/s10052-010-1438-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Omrani, Adel, Rahul Yadav, Guido Link, Timo Lahivaara, Marko Vauhkonen, and John Jelonnek. "Multistatic Uniform Diffraction Tomography Derived Structural-Prior in Bayesian Inversion Framework for Microwave Tomography." IEEE Transactions on Computational Imaging 8 (2022): 986–95. http://dx.doi.org/10.1109/tci.2022.3212835.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Mohite, Siddharth R., Priyadarshini Rajkumar, Shreya Anand, David L. Kaplan, Michael W. Coughlin, Ana Sagués-Carracedo, Muhammed Saleem, et al. "Inferring Kilonova Population Properties with a Hierarchical Bayesian Framework. I. Nondetection Methodology and Single-event Analyses." Astrophysical Journal 925, no. 1 (January 1, 2022): 58. http://dx.doi.org/10.3847/1538-4357/ac3981.

Full text

Abstract:

Abstract We present nimbus: a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on nondetections. This framework makes use of GW 3D distance information and electromagnetic upper limits from multiple surveys for multiple events and self-consistently accounts for the finite sky coverage and probability of astrophysical origin. The framework is agnostic to the brightness evolution assumed and can account for multiple electromagnetic passbands simultaneously. Our analyses highlight the importance of accounting for model selection effects, especially in the context of nondetections. We show our methodology using a simple, two-parameter linear brightness model, taking the follow-up of GW190425 with the Zwicky Transient Facility as a single-event test case for two different prior choices of model parameters: (i) uniform/uninformative priors and (ii) astrophysical priors based on surrogate models of Monte Carlo radiative-transfer simulations of KNe. We present results under the assumption that the KN is within the searched region to demonstrate functionality and the importance of prior choice. Our results show consistency with simsurvey—an astronomical survey simulation tool used previously in the literature to constrain the population of KNe. While our results based on uniform priors strongly constrain the parameter space, those based on astrophysical priors are largely uninformative, highlighting the need for deeper constraints. Future studies with multiple events having electromagnetic follow-up from multiple surveys should make it possible to constrain the KN population further.

APA, Harvard, Vancouver, ISO, and other styles

48

Khan, Nida, and Muhammad Aslam. "Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors." Journal of Quantitative Methods 3, no. 2 (August 31, 2019): 62–76. http://dx.doi.org/10.29145/2019/jqm/030204.

Full text

Abstract:

Bayesian estimation for location parameter of the inverse Gaussian distribution is presented in this paper. Noninformative priors (Uniform and Jeffreys) are assumed to be the prior distributions for the location parameter as the shape parameter of the distribution is considered to be known. Four loss functions: Squared error, Trigonometric, Squared logarithmic and Linex are used for estimation. Bayes risks are obtained to find the best Bayes estimator through simulation study and real life data

APA, Harvard, Vancouver, ISO, and other styles

49

Mohaisen, Ameera Jaber, and Abdulhussein Saber AL-Mouel. "Bayesian One- Way Repeated Measurements Model Based on Bayes Quadratic Unbiased Estimator." JOURNAL OF ADVANCES IN MATHEMATICS 13, no. 2 (April 6, 2017): 7176–81. http://dx.doi.org/10.24297/jam.v13i2.6036.

Full text

Abstract:

In this paper, bayesian approach based on Bayes quadratic unbiased estimator is employed to the linear one- way repeated measurements model which has only one within units factor and one between units factor incorporating univariate random effects as well as the experimental error term. The prior information obtained by using variance analysis technique to represent prior estimates of the parameters of the model. Then, the prior distribution is considered as a uniform distribution.

APA, Harvard, Vancouver, ISO, and other styles

50

Shapiro, Bella. "The last parade uniforms of the Emperor Nicholas II: prior to World War I (unknown document from the State Archive of the Russian Federation)." Genesis: исторические исследования, no. 7 (July 2021): 81–93. http://dx.doi.org/10.25136/2409-868x.2021.7.36125.

Full text

Abstract:

This article introduced into the scientific discourse a previously unknown document from the personal fund of the Emperor Nicholas II stored in the State Archive of the Russian Federation. The document, dedicated to manufacturing of the imperial uniform, is interpreted via examining the sources of personal origin — diaries and memoirs of the last Russian emperor and his immediate circle, authentic uniforms of the Emperor Nicholas II from Moscow and St. Petersburg museums, as well as photographic documents from the Central State Archive of Cinema, Photography, and Audio Documents of St. Petersburg. Methodological framework is based on the chronologically problematic method. In the focus of research is the dynamics of prewar events that cover June – August 1914. Emphasis is also placed on the military representative events of foreign policy nature: parades held during the arrival of the King of Saxony Frederick Augustus III to Russia, as well as the visit of French President Raymond Poincaré. The acquired materials can be valuable for in-depth research on the military history of Russia, history of Russian culture as a whole, history of its tangible culture and costume history. Another area of possible practical implementation is the research work on studying the Russian military uniform in the museum, aimed at fulfillment of its historical-cultural potential and historical uniqueness.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Uniform prior'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles