**19/1, Anna Dreber Albenberg, Stockholm School of Economics: (Predicting) replication outcome**

Abstract: Why are there so many false positive results in the published scientific literature? And what is the actual share of results that do not replicate in different literatures in the experimental social sciences? I will discuss several large replication projects on direct and conceptual replications, as well as our studies on "wisdom-of-crowds" mechanisms like prediction markets and forecasting surveys where researchers attempt to predict replication outcomes as well as new outcomes.

**2/2, Claudia Redenbach, Technische Universität Kaiserslautern: Using stochastic models for segmentation and characterization of spatial microstructures**

Abstract: The performance of engineering materials such as foams, fibre composites or concrete is heavily influenced by the microstructure geometry. Quantitative analysis of 3D images, provided for instance by micro computed tomography (µCT), allows for a characterization of material samples. In this talk, we will illustrate how models from stochastic geometry may support the segmentation of image data and the statistical analysis of the microstructures. Our first example deals with the estimation of the fibre length distribution from µCT images of glass fibre reinforced composites. As examples of segmentation tasks we present the reconstruction of the solid component of a porous medium from focused ion beam scanning electron microscopy (FIB-SEM) image data and the segmentation of cracks in µCT images of concrete.

**16/2, Fredrik Johansson, Chalmers: Making the most of observational data in causal estimation with machine learning**

Abstract: Decision making is central to all aspects of society, private and public. Consequently, using data and statistics to improve decision-making has a rich history, perhaps best exemplified by the randomized experiment. In practice, however, experiments carry significant risk. For example, making an online recommendation system worse could result in millions of lost profits; selecting an inappropriate treatment for a patient could have devastating consequences. Luckily, organizations like hospitals and companies who serve recommendations routinely collect vast troves of observational data on decisions and outcomes. In this talk, I discuss how to make the best use of such data to improve policy, starting with an example of what can go wrong if we’re not careful. Then, I present two pieces of research on how to avoid such perils if we are willing to say more about less.

**2/3, Andrea De Gaetano, IRIB CNR: Modelling haemorrhagic shock and statistical challenges for parameter estimation**

Abstract: In the ongoing development of ways to mitigate the consequences of penetrating trauma in humans, particularly in the area of civil defence and military operations, possible strategies aimed at identifying the victim's physiological state and its likely evolution depend on mechanistic, quantitative understanding of the compensation mechanisms at play. In this presentation, time-honored and recent mathematical models of the dynamical response to hemorrhage are briefly discussed and their applicability to real-life situations is examined. Conclusions are drawn as to the necessary formalization of this problem, which however poses methodological challenges for parameter estimation.

**16/3, Fredrik Lindsten, Linköping University: Monte Carlo for Approximate Bayesian Inference**

Abstract: Sequential Monte Carlo (SMC) is a powerful class of methods for approximate Bayesian inference. While originally used mainly for signal processing and inference in dynamical systems, these methods are in fact much more general and can be used to solve many challenging problems in Bayesian statistics and machine learning, even if they lack apparent sequential structure. In this talk I will first discuss the foundations of SMC from a machine learning perspective. We will see that there are two main design choices of SMC: the proposal distribution and the so-called intermediate target distributions, where the latter is often overlooked in practice. Focusing on graphical model inference, I will then show how deterministic approximations, such as variational inference and expectation propagation, can be used to approximate the optimal intermediate target distributions. The resulting algorithm can be viewed as a post-correction of the biases associated with these deterministic approximations. Numerical results show improvements over the baseline deterministic methods as well as over "plain" SMC.

The first part of the talk is an introduction to SMC inspired by our recent Foundations and Trends tutorial

**30/3, Manuela Zucknick, University of Oslo: Bayesian modelling of treatment response in ex vivo drug screens for precision cancer medicine**

Abstract: Large-scale cancer pharmacogenomic screening experiments profile cancer cell lines or patient-derived cells versus hundreds of drug compounds. The aim of these in vitro studies is to use the genomic profiles of the cell lines together with information about the drugs to predict the response to a particular combination therapy, in particular to identify combinations of drugs that act synergistically. The field is hyped with rapid development of sophisticated high-throughput miniaturised platforms for rapid large-scale screens, but development of statistical methods for the analysis of resulting data is lagging behind. I will discuss typical challenges for estimation and prediction of response to combination therapies, from large technical variation and experimental biases to modelling challenges for prediction of drug response using genomic data. I will present two Bayesian models that we have recently developed to address diverse problems relating to the estimation and prediction tasks, and show how they can improve the identification of promising drug combinations over standard non-statistical approaches.

**6/4, Prashant Singh, Uppsala University: Likelihood-free parameter inference of stochastic time series models: exploring neural networks to enhance scalability, efficiency and performance**

Abstract: Parameter inference of stochastic time series models, such as gene regulatory networks in the likelihood-free setting is a challenging task, particularly when the number of parameters to be inferred is large. Recently, data-driven machine learning models (neural networks in particular) have delivered encouraging results towards addressing the scalability, efficiency and parameter inference quality of the likelihood-free parameter inference pipeline. In particular, this talk will present a detailed discussion on neural networks as trainable, expressive and scalable summary statistics of high-dimensional time series for parameter inference tasks.

Preprint reference: Åkesson, M., Singh, P., Wrede, F., & Hellander, A. (2020). Convolutional neural networks as summary statistics for approximate bayesian computation. arXiv preprint arXiv:2001.11760