Wasserstein Variational Inference

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and the Wasserstein distance as special cases. The gradients of the Wasserstein variational loss are obtained by backpropagating through the Sinkhorn iterations. This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs. Using the Wasserstein variational inference framework, we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques.Comment: 8 pages, 1 figur

The missing link: Predicting connectomes from noisy and partially observed tract tracing data

Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a 'latent space model' that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies

Forward Amortized Inference for Likelihood-Free Variational Marginalization

In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of either the model joint distribution or its derivatives. We prove that our new variational loss is optimized by the exact posterior marginals in the fully factorized mean-field approximation, a property that is not shared with the more conventional reverse KL inference. Furthermore, we show that forward amortized inference can be easily marginalized over large families of latent variables in order to obtain a marginalized variational posterior. We consider two examples of variational marginalization. In our first example we train a Bayesian forecaster for predicting a simplified chaotic model of atmospheric convection. In the second example we train an amortized variational approximation of a Bayesian optimal classifier by marginalizing over the model space. The result is a powerful meta-classification network that can solve arbitrary classification problems without further training.Comment: 9 pages, 3 figure

Patient-reported esthetic outcomes following lower extremity free flap reconstruction: A cross-sectional multicenter study

Introduction: The goal of lower-extremity reconstructions is primarily to salvage the leg; however, esthetic outcomes are also important. This study aimed to assess the impact of a lower extremity free tissue transfer regarding social functioning, patient-reported esthetic outcomes, and possible differences between fasciocutaneous vs. muscle flaps. Material and Methods: For this cross-sectional multicenter study, patients operated between 2003 and 2021, with a minimum follow-up of 12 months, were identified. Outcomes were obtained from 89 patients. Patient-reported outcomes were assessed using a questionnaire containing 5-point Likert scale questions grouped in three groups: aspect of the reconstructed leg, the aspect of the donor site, and the negative impact on social functioning. Physical functioning and mental health were assessed with the Short-Form-36. Results: The overall score for negative impact on social functioning was 22.2. This was 46.7 for the esthetic satisfaction of the reconstructed leg and 57.1 for the donor site. No significant differences were seen between patients who underwent a reconstruction with a fasciocutaneous flap compared to a muscle flap. Secondary surgical procedures for improving the esthetic aspect were performed in 12% of the patients in the fasciocutaneous group and 0% in the muscle group. Conclusion: Our results show that the most optimal esthetic outcome is not defined by the type of flap. We found a strong correlation between physical functioning and the negative impact on social functioning that a reconstructed lower extremity may have. The result of this study can be taken into consideration during the shared decision-making process of choosing the most optimal reconstruction

International lower limb collaborative (INTELLECT) study: a multicentre, international retrospective audit of lower extremity open fractures

Trauma remains a major cause of mortality and disability across the world1, with a higher burden in developing nations2. Open lower extremity injuries are devastating events from a physical3, mental health4, and socioeconomic5 standpoint. The potential sequelae, including risk of chronic infection and amputation, can lead to delayed recovery and major disability6. This international study aimed to describe global disparities, timely intervention, guideline-directed care, and economic aspects of open lower limb injuries

The JASP guidelines for conducting and reporting a Bayesian analysis

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general