我们研究了欧洲排放津贴（EUA）的价格，从而分析了它们对相关能源市场的不确定性和依赖性。我们提出了一个概率的多元条件时间序列模型，该模型利用数据的关键特征。在广泛的滚动窗口预测研究中评估了提议模型和各种竞争模型的预测性能，涵盖了将近两年的样本外。因此，我们预测了30步。多元概率预测的准确性由能量评分评估。鉴于俄罗斯对乌克兰的入侵，我们还讨论了着重于波动性溢出和随时间变化的相关性的发现。

我们提出了一种利用分布人工神经网络的概率电价预测（EPF）的新方法。EPF的新型网络结构基于包含概率层的正则分布多层感知器（DMLP）。使用TensorFlow概率框架，神经网络的输出被定义为一个分布，是正常或可能偏斜且重尾的Johnson的SU（JSU）。在预测研究中，将该方法与最新基准进行了比较。该研究包括预测，涉及德国市场的日常电价。结果显示了对电价建模时较高时刻的重要性的证据。

M5竞争不确定性轨道旨在概率预测成千上万的沃尔玛零售商品。我们表明，M5竞争数据面临强大的过分统计和零星需求，尤其是零需求。我们讨论了对此类计数数据流程的充分概率预测产生的建模问题。遗憾的是，M5竞争中使用的大多数流行预测方法（例如，LightGBM和XGBoost GBMS）无法应对由于所考虑的目标函数而解决数据特征。分布预测为克服这些问题提供了合适的建模方法。 GAMLSS框架允许使用低维分布的灵活概率预测。我们说明了，通过模拟各种分布的位置和比例参数，如何应用GAMLS方法来应用M5竞争数据。负二项分布。最后，我们讨论用于分配建模的软件包及其缺点，如R包Gamls，其中包含其包扩展，（深）分布预测库，如纹orflow概率。

组合和聚合技术可以显着提高预测准确性。这也适用于组合预测分布的概率预测方法。存在几个时变和自适应加权方案，例如贝叶斯模型平均（BMA）。然而，不同预报的质量不仅可以随时间而变化，而且可能在分布范围内变化。例如，在分布的中心，一些分布预测可能更准确，而其他分布预测可能更好地预测尾部。因此，我们介绍了一种新的加权方法，这些方法考虑了随着时间的推移和分布的差异。我们基于跨定量的聚合讨论逐个聚合的点耦合，该算盘相对于连续排序概率得分（CRP）。在分析了点CRPS学习的理论特性之后，我们讨论了基于分位数回归和专家建议的量级回归和预测的批量和在线学习的B型和在线学习的基于B型和在线学习的估算技术。我们证明，拟议的完全自适应伯尔斯坦在线聚合（BOA）用于点CRPS在线学习的方法具有最佳的收敛性。它们在模拟中确认和欧洲排放津贴（EUA）价格的概率预测研究。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clustering interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and explore the trade-off between the sparsity term and the likelihood one with a path following algorithm. The model's behaviour is studied on simulated data and, we show the advantages of the approach on real data benchmark. We also introduce a new data set on financial reports and exhibit the benefits of our method for exploratory analysis.

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.

Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.