Kaip teigiama Statistikos departamento prie Lietuvos Respublikos Vyriausybės, Lietuvoje įkalinimo bausmę, pataisos namuose, atlieka 8000, už įvairius nusikaltimus nuteistų asmenų1. Toks pats, arba labai artimas tam skaičius buvo ir 2009 metais. Pasak, Euro stato2, kriminalinės statistikos duomenų bazės suvestinės sudaro 230 nuteistųjų, 100000 Lietuvos gyventojų. To paties šaltinio teigimu, pagal šį skaičių, Lietuva patenka į pirmą, didžiausią kalinių skaičių turinčių valstybių dešimtuką. Lietuvoje, kaip ir daugumoje postsovietinių šalių, laisvės atėmimas yra dažniausiai taikoma bausmė. Įkalinimo laikas yra pakankamai ilgas, per jį asmuo praranda ryšius su šeima, draugais. Jis nebetenka iki įkalinimo bausmės, turėtų profesinių sugebėjimų, o dažnai jų ir visai nėra turėjęs. Ryšių su artimiausia aplinka praradimas, ilgas įkalinimo bausmės laikas apsunkina šių asmenų sėkmingą integraciją, skatina kartotinį nusikalstamumą.Problema: Lietuvoje įkalinimo bausmės laikas yra ilgas. Per įkalinimo laikotarpį žmogus praranda ryšius su šeima, visuomene, praranda iki įkalinimo turėtus darbinius įgūdžius.Problema: Lietuvoje įkalinimo bausmės laikas yra ilgas. Per įkalinimo laikotarpį žmogus praranda ryšius su šeima, visuomene, praranda iki įkalinimo turėtus darbinius įgūdžius.Nagrinėjama tema yra aktuali visuomenei, kadangi pilniausiai atspindi nusikalstamumo fenomeną. Žmogus, gyvenantis visuomenėje dažniausiai dėl nepalankių aplinkybių šeimoje, artimiausioje aplinkoje, nepakankamai... [toliau žr. visą tekstą] / 1. During the investigation a hypothesis has been confirmed that the repeated criminality of the persons who served imprisonment sentence is connected with the duration of the sentence and the demerit of the successful resocialiasation process of the persons who are punished with the imprisonment sentences. 2. When reviewing scientific literature it has become clear that the state defines the direction of its activity in the following way: it expresses its will through the nation’s legally selected representatives and also seeks to defend the mankind from criminal actions. This is called politics. The population defence is carried out through criminal and sentence prosecution policies. Both of them form social politics. Criminal policy defines criminal actions. Sentence prosecution policy deals with the state Criminal policy but it is being realized in the sentence prosecution and commitment sphere. The aim of these policies is population defence. The state Sentence prosecution policy being carried out through the branch of Penitentiary law is called sentence prosecution or penitentiary law. Penitentiary law deals with social relationships which appear when prosecuting or serving the sentences. As it is obvious from the sources of penitentiary law, the aim of this sentence is not only to put the criminal into prison or to restore legitimacy but also to influence him not to commit crimes in the future. Penitentiary law fulfills its function only in connection with other... [to full text]

The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. The method produces posterior distributions of all cluster parameters and proportions as well as associated cluster probabilities for all objects. We extend this method in several directions to some common but non-standard situations. The first extension covers the case with a few deviant observations not belonging to one of the normal clusters. An extra component/cluster is created for them, which has a larger variance or a different distribution, e.g. is uniform over the whole range. The second extension is clustering of longitudinal data. All units are clustered at all time points separately and the movements between time points are modeled by Markov transition matrices. This means that the clustering at one time point will be affected by what happens at the neighbouring time points. The third extension handles datasets with missing data, e.g. item non-response. We impute the missing values iteratively in an extra step of the Gibbs sampler estimation algorithm. The Bayesian inference of mixture models has many advantages over the classical approach. However, it is not without computational difficulties. A software package, written in Matlab for Bayesian inference of mixture models is introduced. The programs of the package handle the basic cases of clustering data that are assumed to arise from mixture models of multivariate normal distributions, as well as the non-standard situations.

<p>The Bayesian approach to cluster analysis is presented. We assume that all data stem from a finite mixture model, where each component corresponds to one cluster and is given by a multivariate normal distribution with unknown mean and variance. The method produces posterior distributions of all cluster parameters and proportions as well as associated cluster probabilities for all objects. We extend this method in several directions to some common but non-standard situations. The first extension covers the case with a few deviant observations not belonging to one of the normal clusters. An extra component/cluster is created for them, which has a larger variance or a different distribution, e.g. is uniform over the whole range. The second extension is clustering of longitudinal data. All units are clustered at all time points separately and the movements between time points are modeled by Markov transition matrices. This means that the clustering at one time point will be affected by what happens at the neighbouring time points. The third extension handles datasets with missing data, e.g. item non-response. We impute the missing values iteratively in an extra step of the Gibbs sampler estimation algorithm. The Bayesian inference of mixture models has many advantages over the classical approach. However, it is not without computational difficulties. A software package, written in Matlab for Bayesian inference of mixture models is introduced. The programs of the package handle the basic cases of clustering data that are assumed to arise from mixture models of multivariate normal distributions, as well as the non-standard situations.</p>