Recent evidence suggests that individuals with major depressive disorder (MDD) or borderline personality disorder (BPD) not only experience an excess of negative affect but also hold negatively biased internal representations of their affective experiences. Such biases may play an important role in the maintenance of these disorders. However, previous evidence in this area has mostly been based on nonclinical samples. The current experience sampling study compared the momentary affective experiences of 55 individuals with current MDD, 56 individuals with BPD, and 53 healthy individuals with their prior expectations and subsequent memories of these affective experiences. Results showed that individuals with both MDD and BPD considerably overestimated their future and past negative affect. Furthermore, they did not show the optimistic recall bias for positive affect observed in healthy individuals. Notably, the absence of an optimistic bias regarding the expectation of positive affect was specific to depression. Important theoretical and clinical implications are discussed.

Purpose: When studying how factors influence multiple outcomes (e.g., acoustic measures in phonetics), researchers often analyze each outcome separately using a univariate approach. However, this approach ignores relationships between outcomes, which can reduce estimate accuracy and make it difficult to examine how effects are related across outcomes. A multivariate approach addresses these issues by modelling all outcomes jointly. This tutorial illustrates how to fit Bayesian multivariate linear mixed effects models using the R package brms. Method: We present three example applications in phonetic research, using a corpus of Greek utterances. We focus on a rising accent, that is, a deliberate f_0 movement temporally aligned with a word’s stressed syllable and used to highlight that word in speech. Specifically, we examine whether the phonetic characteristics of the rising accent depend on two factors: (a) the presence of a preceding accent within the same utterance, and (b) the location of the stress in the accented word relative to its final syllable. Results: The multivariate approach reduced uncertainty in population-level effect estimates compared to univariate models and provided a convenient way to examine correlations among effects across outcomes. Conclusions: This tutorial provides guidance on implementing Bayesian multivariate linear mixed effects models and demonstrates their potential.

Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve a favorable combination of both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling via generative neural networks to slower but guaranteed-accurate MCMC when needed. By reusing computations across steps, our workflow synergizes amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on several synthetic and real-world problems with tens of thousands of datasets, showing efficiency gains while maintaining high posterior quality.

Analyzing the mean response of study subjects in psychological research is a standard, well-justified practice. However, theoretical arguments and empirical evidence also suggest that there is value in investigating other aspects of the distribution of such responses, such as their variability or skewness. A particular challenge that practitioners face is statistical modeling of associations between distributional features and other outcomes of interest. The most common approach is to perform estimation in two steps: distributional features are estimated first, and then those estimates are used as predictors for the relevant outcomes. Such an approach is most amenable to implementation in standard statistical software, but it ignores estimation error and can therefore lead to biased estimates and increased error rates. We introduce Distributional Feature Latent Variable Models (DFLVM), a general framework that represents between-person difference in distributional features as random intercepts. These intercepts can be simultaneously used as predictors for downstream outcomes and their associations estimated in a single estimation step. We compare the performance of our approach against two-step procedures in a simulation study and through a re-analysis of a real dataset.

Simulation-based calibration checking (SBC) refers to the validation of an inference algorithm and model implementation through testing self-consistency with repeated inference on data simulated from a generative model. In the original and commonly used approach, the generative model uses parameters drawn from the prior, and thus the approach is testing whether the inference works for simulated data generated with parameter values plausible under that prior. This approach is natural and desirable when we want to test whether the inference works for a wide range of datasets we might observe. However, after observing data, we are interested in answering whether the inference works conditional on that particular data. In this paper, we propose posterior SBC and demonstrate how it can be used to validate the inference conditionally on observed data. We illustrate the utility of posterior SBC in three case studies: (1) A simple multilevel model; (2) a model that is governed by differential equations; and (3) a joint integrative neuroscience model which is approximated via amortized Bayesian inference with neural networks.

Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation and even directly in model inference. Over the past decades, the application areas and overall potential of simulations in statistical workflows have expanded significantly, driven by the development of new simulation-based algorithms and exponentially increasing computational resources. In this paper, we examine past and current trends in the field and offer perspectives on how simulations may shape the future of statistical practice. This article is part of the theme issue 'Statistical workflow'.

Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging. Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI to graph data to address these challenges to perform inference on node-, edge-, and graph-level parameters. Our approach couples permutation-invariant graph encoders with flexible neural posterior estimators in a two-module pipeline: a summary network maps attributed graphs to fixed-length representations, and an inference network approximates the posterior over parameters. In this setting, several neural architectures can serve as the summary network. In this work we evaluate multiple architectures and assess their performance on controlled synthetic settings and two real-world domains - biology and logistics - in terms of recovery and calibration.

Gaussian processes are a powerful class of non-linear models, but have limited applicability for larger datasets due to their high computational complexity. In such cases, approximate methods are required, for example, the recently developed class of Hilbert space Gaussian processes. They have been shown to significantly reduce computation time while retaining most of the favorable properties of exact Gaussian processes. However, Hilbert space approximations have so far only been developed for uni-dimensional outputs and manifest (known) inputs. Thus, we generalize Hilbert space methods to multi-output and latent input settings. Through extensive simulations, we show that the developed approximate Gaussian processes are indeed not only faster, but also provide similar or even better uncertainty calibration and accuracy of latent variable estimates compared to exact Gaussian processes. While not necessarily faster than alternative Gaussian process approximations, our new models provide better calibration and estimation accuracy, thus striking an excellent balance between trustworthiness and speed. We additionally illustrate our methods on a real-world case study from single cell biology.