# Bayesian Methods for Estimation the Parameters of Finite Mixture of Inverse Rayleigh Distribution

Authors: Fadhaa Ali
Journal: Mathematical Problems in Engineering
 (2023)

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## Abstract

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and Metropolis – Hastings algorithms. The proposed techniques are applied to simulated data following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). The results showed that the method was well performed in all simulation scenarios with respect to different sample sizes.


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## Body

## 1. Introduction

Applications of the finite mixture model have increasingly been considered in many fields when data does not belong to a specific distribution. This can result from mixing several subpopulations in an unknown proportion to form one large population. Therefore, the commonly available distributions cannot be fitted to the data under study. As an example, the mixture model can be fitted to data that comes from many fields, such as engineering, physical sciences, biological sciences, chemical sciences, and other fields.A mixture of normal and Laplace distributions is used to model wind shear data [1, 2]. Moreover, the crime and justice data also take part in the applications of the mixture model [3]. The failure time be modeled by the mixture of exponential distribution by which the whole population is divided into several subpopulations [4]. In the studies of image compression and pattern recognition, the Gaussian mixture model is used to improve the likelihood of the model by split-and-merge operations. This is of course depending on the nature of the data, as far as inferring some characteristics about it is concerned [5]. A mixture of t-distributions is proposed to model a set of data containing sets of observations with longer than normal tails [6]. In the blood sciences, the mixture model also played an important role in classifying individuals based on estimated density functions for each individual. In doing so, a classifier was built to classify new unlabeled histograms into normal and iron deficient classes of red blood cells (RBCs) [7].A mixture model can be framed from densities following different families of distribution or the same ones. In either way, it is useful as a predicting or confirmatory tool. In fact, the classical method for inferring the model is by maximizing the likelihood (ML) that is used in a wide range of cases [8, 9]. However, the problematic issue is the unknown number of allocations and observation membership by which the accuracy of estimation is undermined. These issues have been tackled by many proposed techniques over the years. Testing the number of components to determine the exact structure of the mixture model was a vital tool to deal with the above problem [10, 11]. In much the same direction, the most used way of handling the above issues is adding a latent variable that results in a complete data log likelihood rather than using the incomplete one [12, 13], and then estimating the model parameters by the expectation maximization(EM) algorithm [14–16].Moreover, the component indicator labels can be adopted to analyze dependent data by employing the Markov hidden model, which has been considered as an extension of the mixture model. This can clearly be seen in a study proposed to fit a mixture of auto-regressive of dependent data [17], or to model of nonlinear time series [18].Bayesian inference is conducted in many studies in which inferring the model is applied by assuming prior information about the parameters and then using posterior simulation via Markov chain Monte Carlo (MCMC) methods [19, 20]. This framework of estimation can avoid many difficulties upon its applications that involve (ML) estimation [21–23].In the lifetimes of certain engineering processes, some methods have been proposed to introduce mixtures of inverse Rayleigh distribution, one of which is used to model the heterogeneity existing in the lifetime processes by the Bayesian inference of the mixture model using noninformative (the Jeffreys and the uniform) and informative (gamma) priors by considering censoring data as an example for their work [24]. In the same direction, another method has been introduced to fit a 3-component mixture of the inverse Rayleigh distributions under a Bayesian perspective to a censored sampling scheme by the use of noninformative and informative priors under different loss functions [25, 26] introducing a general form of inverse Rayleigh distribution which is known as exponentiated inverse Rayleigh distribution (EIRD), which extends a more flexible distribution for modeling life data.In this paper, we derived the posterior distributions of a mixture of inverse Rayleigh distributions. We then estimated model parameters by the use of Gibbs Sampler and the Metropolis ̶Hastings algorithms. The two algorithms are used with this model and noncensoring data for the first time. Classifying observations according to their groups is also considered in this article, which has not been considered in the literature till the date of this work. Thereafter, the accuracy of the model inferring has been examined by simulation based on different scenarios and a number of components. The success rate of clustering is also calculated accordingly. The rest of the paper is organized to give the theoretical details of the theoretical part in Section2. In Section 3, the method is applied to simulated data by using several scenarios. In Sections 4 and 5, discussion and conclusion are involved to show the complete picture of the method and its results.


## 2. Methods

A mixture of inverse Rayleigh distributions is used to model a variety of data that come from more than or equal two-components. Data can be a failure time or working time of an equipment. Therefore, inference about its parameters is vital as far as maintenance is concerned. The probability density function of the inverse Rayleigh distribution is written as follows:(1)fx;λ=2λx3e−λ/x2,wherex,λ>0.In many lifetime scenarios, data can come from more than one component, and then the known distributions become inaccurate to fit the data. In such a case, the mixture model can be used to handle the problem. When it comes to lifetime data, a finite mixture of the Rayleigh distribution is used to model the data [27]. The probability density function (p.d.f) of the mixture of the Rayleigh distribution with k components can be written as follows:(2)fxi;θ=∑j=1kwj2λjxi3e−λj/xi2,where ∑j=1kwj=1,0<wj<1,θ=w1,w2,…,wk−1,λ1,λ2,…,λkT, x,λ>0.


## 3. Inferential Method

The traditional way of estimating the parameters of a mixture model is by maximizing the likelihood function of the model. The incomplete data likelihood function of the k-components mixture model is defined as follows:(3)Lθ|X=∏i=1n∑j=1kwj2λjxi3e−λj/xi2,where X=x1,x2,…,xnT.To this end, an unknown number of components is considering as a hidden state that needs to be defined by a latent vectorZ, whereas Z=Z1,Z2,…,ZnT. Here, Zi=zi1,zi2,…,zikT and zij is defined as follows:(4)zij=1,ifitemibelongtocomponentj,0,otherwise.Hence, the complete data likelihood function is written as follows:(5)Lθ|X,Z=∏i=1n∏j=1kwj2λjxi3e−λj/xi2zij.LetSj represent the set of all items of the component j, where(6)Sj=xi;zij=1,i=1,2,…,n.Simplifying the equation (4) leads to(7)Lθ|X,Z=w1n1w2n2…wknk2λ1n1.2λ2n2…2λknk1∏i=1nxi3e−∑j=1kz1jλj/x12.e−∑j=1kz2jλj/x22….e−∑j=1kznjλj/xn2,where nj=⋕Sj.Many methods have been developed to estimate the parameters of equation (5). Generally, the most popular one is the expectation-maximization algorithm that is used with some mixture models [28]. In this paper, the Bayesian framework is developed to estimate the model parameters. In so doing, proper priors are used to derive the posterior distribution of the parameters using the Bayesian framework. The next section consists of the details of such methods.


### 3.1. Bayesian Framework

To apply Bayesian techniques, we have to consider proper priors for the parameters. To do so, letW∼Dirα1,α2,…,αk,whereW=w1,w2,…,wkT, and the probability density function is written as follows:(8)gW;α_=Γ∑j=1kαj∏j=1kΓαj∏j=1kwjαj−1.We also let(9)λj∼Gammaη,β,where>0,β>0,and the probability density function is written as follows:(10)hλj;η,β=1Γηβηλjη−1e−λj/β.The joint probability density function is formulated by multiplying equations (5), (6), and (10), which results in(11)pθ|X,Z∝Lθ|X,ZΓ∑j=1kαj∏j=1kΓαj∏j=1kwjαj−1∏j=1k1Γηβηλjη−1e−λj/β.Simplifying the above equation will lead to(12)pθ|X,Z∝δΓ∑j=1kαj+nj∏j=1kΓ∑j=1kαj+nj∏j=1kwjnj+αj−11Γηβηλjnj+η−1e−λj∑i=1nxi−2+β−1.Hence, the posterior distribution ofλj is(13)λj|∼Gammanj+η,1∑i=1nxi−2+β−1.In the same way, the posterior distribution ofW is as follows:(14)W∼Dirα1+n1,α2+n2,…,αk+nk.The probability that the itemi belongs to the component j can be determined by the below formula:(15)p^ij=PZij=1|λ^j,w^j,X=w^jfxi,λ^j∑j=1kw^jfxi,λ^j,where p^i=p^i1,p^i2,…,p^ik, and λ^j,w^j can be calculated by the mathematical expectations of the posterior distributions. The observation membership is then generated from the multinational distribution according to Z^i∼Mult1,p^i.Sj is also updated according to the estimated Z^i.Note that the above framework is based on known allocationSj. Therefore, we use one of the well-known clustering algorithms to give us the possible number of mixture components and their item memberships zi. For this framework, we used the K-means algorithm [29] to determine the initial allocation, where k = 1, 2, …., K. Afterward, we assessed the appropriate number of components by calculating the Bayesian information criteria [13] according to the formula(16)BIC=−2Lθ+dlogn.Observation memberships can be updated after getting Bayesian estimators by usingMult1,p^i as mentioned above. Having updated the observation memberships and their components, model parameters can be evaluated by the mathematical expectations of the posterior distribution in equations (13) and (14). Of course, the above framework can be inaccurate in terms of finding the exact memberships of observations that the framework depends on. Therefore, we solved the problem by employing two iterative algorithms.


### 3.2. Gibbs Sampler

Bayesian framework for estimating parameters of a mixture of Inverse Rayleigh distribution is used by emplying Gibbs sampler given that it is considered as Markov chain Monte Carlo (MCMC). This scheme estimates the augmented parameterS,θ by sampling from the complete data posterior distribution pS,θ|X. To this end, the complete data posterior is proportional to the complete data likelihood defined in equation (12). The steps of the Gibbs sampler algorithm start with some initial classification S0 and fixed parameters α,η,β, where j=1,2,…,k and S0=S10,S20,…,Sk0. Then, the following steps are repeated for m=1,2,…,M.(a)
Conditional on the classificationSm−1,αj, the weight parameters w1m,w2m,…,wkm can be sampled from the posterior distribution, which represents the Dirichlet distribution Dirα1+n1m−1,α2+n2m−1,…,αk+nkm−1, where njm−1=⋕Sjm−1.(b)
Sample the component parametersλjm from the posterior distribution, which represents njm−1+η,1/β−1+∑i=1njm−1xi−2.(c)
Calculatingpijm by equation (15) as shown below:(17)pijm=wjmfxi,λjm∑j=1kwjmfxi,λjm.(d)
Updating the classification of each observationxi conditional on knowing wjm,λjm,pim by sampling zjm independently for each i=1,2,…,n from the conditional distribution zim∼Mult1,pim, which leads to the new classifications.(18)Sjm=xi;zijm=1,i=1,2,…,n.Increasem by one, and return to step (a).


### 3.3. Metropolis Hastings

A Metropolis ̶Hastings algorithm is an important tool to generate a sample from the mixture posterior distribution (Celeux et al. [22]; Brooks [23]). Here, the posterior distributions of λj,W as in equations (10) and (11) will be used. This algorithm starts with some initial classification S0 and some initial values for the parameters W0,λj0 and fixed parameters αj,η,β, where j=1,2,…,k and S0=S10,S20,…,Sk0. We then repeated the following steps for m=1,2,…,M.(a)
(i) Propose a new parameterλjnew by sampling from a proposal density q1λj|λjm−1. Here q1 will represent normal distribution density.(ii) Propose a new parameterwnew, where wnew=w1new,w2new,…,wknewT by sampling from a proposal density q2w|wm−1, such that ∑j=1k−1wjnew<1, where j=1,2,…,k−1. We then calculate wknew=1−∑j=1k−1wjnew. Here q2 will be defined in (b).(b)
(i) Move the sampler toλjnew with probability min1,Aj, where(19)Aj=pλjnew;X,η,βq1λjm−1|λjnewpλjm−1;X,η,βq1λjnew|λjm−1.Ifu1<min1,Aj, where u1 is a random number from the uniform distribution U0,1, then accept λjnew and set λjm=λjnew; otherwise, reject λjnew and set λjm=λjm−1. Note that the quantity pλjnew;X,η,β is the density value of the posterior distribution with the parameters shown in equation (13).(ii) Move the sampler toWnew with probability min1,V, where(20)V=∅Wnew;αq2Wm−1|Wnew∅Wm−1;αq2Wnew|Wm−1.Ifu2<min1,V, where u2 is a random number from the uniform distribution U0,1, then accept Wnew and set Wm=Wnew, otherwise reject Wnew and set Wm=Wm−1. Note that the quantity ∅ is the density value of the posterior distribution shown in equation (14). Here, q2 is defined as q2W|W∗=∏j=1kφwj|wj∗, where φwj|wj∗ is a density value of U0,wj∗.(c)
Calculatingpijm by equation (15) as shown:(21)pijm=wjmfxi,λjm∑j=1kwjmfxi,λjm.(d)
Updating the classification of each observationxi conditional on knowing wjm,λjm,pim by sampling Zim independently for each i=1,2,…,n from the multinomial distribution zim∼Mult1,pim, which leads to the new classification Sjm=xi;zijm=1,i=1,2,…,n.Increasem by one and return to step (a).


## 3.1. Bayesian Framework

To apply Bayesian techniques, we have to consider proper priors for the parameters. To do so, letW∼Dirα1,α2,…,αk,whereW=w1,w2,…,wkT, and the probability density function is written as follows:(8)gW;α_=Γ∑j=1kαj∏j=1kΓαj∏j=1kwjαj−1.We also let(9)λj∼Gammaη,β,where>0,β>0,and the probability density function is written as follows:(10)hλj;η,β=1Γηβηλjη−1e−λj/β.The joint probability density function is formulated by multiplying equations (5), (6), and (10), which results in(11)pθ|X,Z∝Lθ|X,ZΓ∑j=1kαj∏j=1kΓαj∏j=1kwjαj−1∏j=1k1Γηβηλjη−1e−λj/β.Simplifying the above equation will lead to(12)pθ|X,Z∝δΓ∑j=1kαj+nj∏j=1kΓ∑j=1kαj+nj∏j=1kwjnj+αj−11Γηβηλjnj+η−1e−λj∑i=1nxi−2+β−1.Hence, the posterior distribution ofλj is(13)λj|∼Gammanj+η,1∑i=1nxi−2+β−1.In the same way, the posterior distribution ofW is as follows:(14)W∼Dirα1+n1,α2+n2,…,αk+nk.The probability that the itemi belongs to the component j can be determined by the below formula:(15)p^ij=PZij=1|λ^j,w^j,X=w^jfxi,λ^j∑j=1kw^jfxi,λ^j,where p^i=p^i1,p^i2,…,p^ik, and λ^j,w^j can be calculated by the mathematical expectations of the posterior distributions. The observation membership is then generated from the multinational distribution according to Z^i∼Mult1,p^i.Sj is also updated according to the estimated Z^i.Note that the above framework is based on known allocationSj. Therefore, we use one of the well-known clustering algorithms to give us the possible number of mixture components and their item memberships zi. For this framework, we used the K-means algorithm [29] to determine the initial allocation, where k = 1, 2, …., K. Afterward, we assessed the appropriate number of components by calculating the Bayesian information criteria [13] according to the formula(16)BIC=−2Lθ+dlogn.Observation memberships can be updated after getting Bayesian estimators by usingMult1,p^i as mentioned above. Having updated the observation memberships and their components, model parameters can be evaluated by the mathematical expectations of the posterior distribution in equations (13) and (14). Of course, the above framework can be inaccurate in terms of finding the exact memberships of observations that the framework depends on. Therefore, we solved the problem by employing two iterative algorithms.


## 3.2. Gibbs Sampler

Bayesian framework for estimating parameters of a mixture of Inverse Rayleigh distribution is used by emplying Gibbs sampler given that it is considered as Markov chain Monte Carlo (MCMC). This scheme estimates the augmented parameterS,θ by sampling from the complete data posterior distribution pS,θ|X. To this end, the complete data posterior is proportional to the complete data likelihood defined in equation (12). The steps of the Gibbs sampler algorithm start with some initial classification S0 and fixed parameters α,η,β, where j=1,2,…,k and S0=S10,S20,…,Sk0. Then, the following steps are repeated for m=1,2,…,M.(a)
Conditional on the classificationSm−1,αj, the weight parameters w1m,w2m,…,wkm can be sampled from the posterior distribution, which represents the Dirichlet distribution Dirα1+n1m−1,α2+n2m−1,…,αk+nkm−1, where njm−1=⋕Sjm−1.(b)
Sample the component parametersλjm from the posterior distribution, which represents njm−1+η,1/β−1+∑i=1njm−1xi−2.(c)
Calculatingpijm by equation (15) as shown below:(17)pijm=wjmfxi,λjm∑j=1kwjmfxi,λjm.(d)
Updating the classification of each observationxi conditional on knowing wjm,λjm,pim by sampling zjm independently for each i=1,2,…,n from the conditional distribution zim∼Mult1,pim, which leads to the new classifications.(18)Sjm=xi;zijm=1,i=1,2,…,n.Increasem by one, and return to step (a).


## 3.3. Metropolis Hastings

A Metropolis ̶Hastings algorithm is an important tool to generate a sample from the mixture posterior distribution (Celeux et al. [22]; Brooks [23]). Here, the posterior distributions of λj,W as in equations (10) and (11) will be used. This algorithm starts with some initial classification S0 and some initial values for the parameters W0,λj0 and fixed parameters αj,η,β, where j=1,2,…,k and S0=S10,S20,…,Sk0. We then repeated the following steps for m=1,2,…,M.(a)
(i) Propose a new parameterλjnew by sampling from a proposal density q1λj|λjm−1. Here q1 will represent normal distribution density.(ii) Propose a new parameterwnew, where wnew=w1new,w2new,…,wknewT by sampling from a proposal density q2w|wm−1, such that ∑j=1k−1wjnew<1, where j=1,2,…,k−1. We then calculate wknew=1−∑j=1k−1wjnew. Here q2 will be defined in (b).(b)
(i) Move the sampler toλjnew with probability min1,Aj, where(19)Aj=pλjnew;X,η,βq1λjm−1|λjnewpλjm−1;X,η,βq1λjnew|λjm−1.Ifu1<min1,Aj, where u1 is a random number from the uniform distribution U0,1, then accept λjnew and set λjm=λjnew; otherwise, reject λjnew and set λjm=λjm−1. Note that the quantity pλjnew;X,η,β is the density value of the posterior distribution with the parameters shown in equation (13).(ii) Move the sampler toWnew with probability min1,V, where(20)V=∅Wnew;αq2Wm−1|Wnew∅Wm−1;αq2Wnew|Wm−1.Ifu2<min1,V, where u2 is a random number from the uniform distribution U0,1, then accept Wnew and set Wm=Wnew, otherwise reject Wnew and set Wm=Wm−1. Note that the quantity ∅ is the density value of the posterior distribution shown in equation (14). Here, q2 is defined as q2W|W∗=∏j=1kφwj|wj∗, where φwj|wj∗ is a density value of U0,wj∗.(c)
Calculatingpijm by equation (15) as shown:(21)pijm=wjmfxi,λjm∑j=1kwjmfxi,λjm.(d)
Updating the classification of each observationxi conditional on knowing wjm,λjm,pim by sampling Zim independently for each i=1,2,…,n from the multinomial distribution zim∼Mult1,pim, which leads to the new classification Sjm=xi;zijm=1,i=1,2,…,n.Increasem by one and return to step (a).


## 4. Simulations

To apply the methods we proposed, data were generated in various cases. Here, to save the time and the area, we generate the data according to a two-component and three-component mixture of inverse Rayleigh distributions. The following scenarios were used to generatexi according to predefined parameters for the two-components model θ1=w1,λ1,λ2T, where w1,λ1,λ2=0.3,2,16,0.3,4,12,0.3,3,20,0.3,2,10, whereas the following scenarios were used to generate xi according to predefined parameters for three-components model θ2=w1,w2,λ1,λ2,λ3T, where w1,w2,λ1,λ2,λ3=0.3,0.4,1,10,20, 0.3,0.4,2,10,16,0.3,0.4,5,15,30,0.3,0.4,1,4,7, and sample sizes n=90,120,200.A comparison was made by calculating the average of the mean square error (AMSE) of the estimated model and the average of the classification success rate (ACSR) for the number of replications equal toR by using the following formulas.(22)MSEr=∑i=1nfxi;λ−fxi;λ^r2nThe average of MSE of all replications can be computed asAMSE=∑r=1RMSEr/R where MSEr is the MSE of the model on the replication rth. For calculating ACSR, we let Ar be the number of items with correctly estimated membership divided by the sample size n on the replication rth,(23)ACSR=∑r=1RArR,where R is the number of replications.The results of Table1 show that the two methods are good in terms of the AMSE. However, when it comes to the ACSR, the Gibbs sampler method shows high accuracy in classifying items to their true components specially when the difference between components parameters are large enough. However, the MH method is quietly outperforming the others when the component parameters are close in their value. The graphs of the p.d.f. of the model with respect to the true parameters and the estimators are shown in Figure 1. It can be seen that as the sample size increased, the accuracy of the estimation increased, as it obvious from the graphs when it became close to the true one.Table 1
This table shows the results of AMSE and ACSR of applying the three methods to simulated data by three different scenarios written at the top and three different of sample sizes with 1000 replications.
NMethodπ1=0.3,θ1=2,θ2=16π1=0.3,θ1=4,θ2=12π1=0.3,θ1=3,θ2=20π1=0.3,θ1=2,θ2=10AMSEACSRAMSEACSRAMSEACSRAMSEACSR100Gibbs0.001790.86030.002260.75760.001240.84380.002920.7926MH0.001570.79590.002630.66740.001180.77590.002980.7252150Gibbs0.001720.86270.002190.7530.001230.8450.0030.7875MH0.001520.79530.002420.67180.001160.77460.002870.7327200Gibbs0.001740.86540.002240.73980.001260.85290.002950.7982MH0.001540.79920.002430.6660.001180.781720.002840.7326Figure 1
Figure shows the graphs of the p.d.f of the model, given the true parameters and the estimated ones by the three methods. The columns of graphs from the left to the right represent the sample sizesn=90,120,200, respectively. The rows of graphs from the top to the bottom represent the parameters scenarios w1,λ1,λ2=0.3,2,16,0.3,4,12,0.3,3,20,and0.3,2,10, respectively.The results of Table2 show that all methods are good in terms of the AMSE. However, when it comes to the ACSR, the Gibbs sampler method shows high accuracy in classifying items to their true components specially when the difference between component parameters is large enough. However, the MH method is quietly outperforming the others when the component parameters are close in their value. The graphs of the p.d.f. of the model with respect to the true parameters and the estimators are shown in Figure 2. It can be seen that as the sample size increased, the accuracy of the estimation increased as it was obvious from the graphs when it became close to the true one.Table 2
This table shows the results of AMSE and ACSR of applying the three methods to simulated data by three different scenarios written at the top and three different of sample sizes with 1000 replications.
NMethodπ1=0.3,π2=0.4,θ1=1,θ2=10,θ3=20π1=0.3,π2=0.4,θ1=2,θ2=10,θ3=16π1=0.3,π2=0.4,θ1=5,θ2=15,θ3=30π1=0.3,π2=0.4,θ1=1,θ2=4,θ3=7AMSEACSRAMSEACSRAMSEACSRAMSEACSR100Gibbs0.013270.58420.016340.52260.009010.47920.043490.5137MH0.013150.53080.018650.4490.009950.4240.052150.4458150Gibbs0.013160.58150.016420.520.0090.47720.043370.5125MH0.013130.52950.018520.45980.009990.42290.04990.4447200Gibbs0.013120.57570.016810.51850.009230.46860.043630.51625MH0.01310.53770.01850.45890.010.42210.049620.444Figure 2
Figure shows the graphs of the p.d.f of the the model, given the true parameters and the estimated ones by the three methods. The columns of graphs from the left to the right represent the sample sizesn=90,120,200, respectively. The rows of graphs from the top to the bottom represent the parameters scenarios w1,w2,λ1,λ2,λ3T, where w1,w2,λ1,λ2,λ3=0.3,0.4,1,10,20,0.3,0.4,2,10,16,0.3,0.4,5,15,30,and0.3,0.4,1,4,7, respectively.


## 5. Example on Real Data

The two methods that have been proposed in this study were applied to one type of elevators, which is so called Arkel. The data was represented the failure time in days of the two elevators of the same type in two 8-story buildings in Baghdad. The data of each elevator was labeled so that one can recognize each observation belong to which elevator. We then mix the observations. We assume that we do not know the certain allocation of the model. We assess the number of the components by applying the BIC. It can be seen from Table3 that the lower value of BIC is 1168.299, which means that the observations belong to two groups. Afterward, we inferred the mixture model by applying the two methods and calculate the classification error rate Ar for both methods. Table 4 shows the estimators and the Ar of each method. The results were close to each other with an acceptable classification error rate Ar. The graph of fx;θ is also shown in Figure 3.Table 3
This table shows the results of BIC of the real data example.
K = 2K = 3K = 4K = 51168.2991187.4721201.1341213.734Table 4
This table shows the results of BIC of the real data example.
Methodλ1λ2w1w2ArGibbs2.95960710.5101650.18870820.81129180.68MH3.51459610.9823490.26115990.73884010.66Figure 3
Figure shows the graphs of the p.d.f of the mixture model of the real data.


## 6. Conclusion and Discussion

We employed two iterative algorithms to infer the mixture of the inverse Rayleigh distribution by using conjugate priors. The novelty of this work is by deriving the estimator formulas according to the Bayesian framework for a finite mixture of the inverse Rayleigh distribution, which has not been done by anyone in the literature. This technique can be applied to any number of components for the finite mixture of inverse Rayleigh distributions. From this study, we conclude that using proper priors in Bayesian methods can result in accurate estimators and lead to the best classification of observations into their right clusters. The main aim of this work is to derive the mathematical formulas of the model estimators when we havek components. We proved these formulas in the simulation part by taking two and three components only as possible examples. Of course, we can apply to any value of k, but bear in mind that as k is increased, we need larger sample sizes, which takes a very long time when we run the code plus we have to involve more tables, more figures, and more space, which leads to an increase in the number of pages. In this work, in each scenario, we simulate a completely different data sets and replicated that for 1000 times. So it is not the matter that AMSE has to get smaller than the previous smaller sample sizes because the algorithm is run on different data sets. However, we achieved that in the results, which means that the applications were precise as far as estimators are concerned. Regarding the figures, in comparing the estimated curve of the pdf to the one with true values, we think the most important thing is getting a similar shapes. This is also difficult to get closer curves because we simulated different data sets for different sample sizes, which it is believed that it is more justified stuff than generating one large sample size and drawing from it sample sizes 90,120, and 200. The latter one, will of course result in closer curves as sample size increased. Note that, choosing sample sizes is completely arbitrary. When the sample size is small like n = 30, the accuracy of estimators will be decreased. For example, if we have k = 3 and n = 30, maybe one of the components will include a few observations, which makes estimating component parameters inaccurate.



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*Source: 2912584-2023-01-11.xml*