How many data clusters are in the Galaxy data set?: Bayesian cluster analysis in action.

In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287-304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin's concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters.

First-episode psychotic disorders in the wake of the COVID-19 pandemic: a descriptive review of casereports.

Objective: Since the onset of COVID-19 pandemic, many case reports and case series dealt with new-onset psychotic disorders in patients either infected with Our aim was to provide a comprehensive collection of these reports to illustrate the nature of these psychoses.

Methods: We conducted a literature search in MEDLINE, Embase, PsycINFO, using search terms regarding first-episode psychotic disorders in the context of corona.

Results: 96 case reports or case series covering 146 patients (62 without and 84 with SARS-CoV-2 infection) were found.

Lack of association between cortical amyloid deposition and glucose metabolism in early stage Alzheimer´s disease patients.

Background: Beta amyloid (Aβ) causes synaptic dysfunction leading to neuronal death. It is still controversial if the magnitude of Aβ deposition correlates with the degree of cognitive impairment. Diagnostic imaging may lead to a better understanding the role of Aβ in development of cognitive deficits.

Semi-parametric Regression under Model Uncertainty: Economic Applications.

Economic theory does not always specify the functional relationship between dependent and explanatory variables, or even isolate a particular set of covariates. This means that model uncertainty is pervasive in empirical economics. In this paper, we indicate how Bayesian semi-parametric regression methods in combination with stochastic search variable selection can be used to address two model uncertainties simultaneously: (i) the uncertainty with respect to the variables which should be included in the model and (ii) the uncertainty with respect to the functional form of their effects.

From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering.

In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303-324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than with high probability.

Identifying Mixtures of Mixtures Using Bayesian Estimation.

The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability.

Treatment Duration With Long-Acting Injectable Antipsychotics After In-hospital Initiation: A Retrospective Cohort Study.

Purpose: Long-acting injectable (LAI) antipsychotics are recommended especially for patients with multiple admissions and poor adherence. The empirical basis of this strategy is a matter of debate.

Methods: In a retrospective cohort study extending over 6 years, all patients admitted for inpatient treatment with a diagnosis of psychotic disorders according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision (F2) were screened for treatment episodes with a new start of an LAI.

Model-based clustering based on sparse finite Gaussian mixtures.

In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC.

Mood-dependent changes of serum lithium concentration in a rapid cycling patient maintained on stable doses of lithium carbonate.

Objective: Serum lithium levels may be influenced by mood state. We report on a 58-year-old female patient suffering from rapid cycling bipolar disorder. Her serum lithium levels varied greatly, despite stable medication.