Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous...
This paper investigates the feasibility of detecting and estimating the rate of internal hemorrhage based on continuous noninvasive hematocrit measurement. A unique challenge in hematocrit-based hemorrhage detection is that hematocrit decreases in re...
Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time. A Bayesian nonparametric model known as the Wishart process has been shown to be effective in this situ...
Sequential systems are commonly analysed with probabilistic, state-space, dynamical and learning-based methods, yet these approaches may define state spaces broader than the states that an active system can actually reach under structural constraints...
Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting,...
The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to...
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain...
We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past...
Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS...
Inferring demographic history from whole-genome data is a central objective in evolutionary and conservation genomics. However, the Pairwise Sequentially Markovian Coalescent (PSMC) framework, one of the most widely used demographic inference methods...
Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence that their associated inference models are oftentimes too limited and...
Urban forests play a crucial role in both emitting and absorbing atmospheric pollutants. Understanding the ecological processes of biogenic volatile organic compounds (BVOCs) and secondary organic aerosols (SOAs) and their interactions in urban fores...
Tree ensembles—Random Forests (RFs) and Gradient Boosting Machines (GBMs)—often stabilize before all trees are evaluated. We study early stopping as a nonparametric change-point problem on prediction increments. The P2-STOP method family...
This study aims to explore the effect of intermittent sequential pneumatic compression (ISPC) intervention on the coupling relationship between arterial blood pressure (ABP) and changes in oxyhaemoglobin (Δ [O2Hb]). The coupling strength between the...
Though accelerometers for condition diagnosis of a bearing is preferably placed at the nearest position of the bearing as possible, in some plant equipment, the accelerometer is difficult to set near the diagnosed bearing, and in many cases, sensors...
Treeensembles usually evaluate every tree or boosting stage for every input, even when the partial prediction has already stabilized. We study inference-time lazy evaluation for pre-trained random forests and gradient-boosted decision trees. LazyRF s...
We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the var...
Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of...
Fault diagnosis in multistage manufacturing processes (MMPs) is a challenging task where most of the research presented in the literature considers a predefined inspection scheme to identify the sources of variation and make the process diagnosable....
This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fu...
Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has be...
Precise odor source localization in turbulent and sparse environments plays a vital role in enabling robotic systems for hazardous chemical monitoring and effective disaster response. To address this, we propose Cooperative Gravitational-Rényi...
Predictive models can rank low-risk cases, but past accuracy cannot establish that skipping review is safe in the current batch. We formulate selective release as a risk-constrained finite-population audit design. A model-proposed candidate and its a...
The book amounts are modeled as values of a random variable, represented by a mixture of distributions of both the correct and error-contaminated amounts. The mixing coefficient represents the proportion of items with non-zero error amounts. This stu...
Recently, some state-of-the-art works have used deep learning-based architectures, specifically convolutional neural networks (CNNs), for banknote recognition and counterfeit detection with promising results. However, it is not clear which design str...
Learning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demon...
Variational algorithms have gained prominence over the past two decades as a scalable computational environment for Bayesian inference. In this article, we explore tools from the dynamical systems literature to study the convergence of coordinate asc...
This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycl...
This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic...
In a model of simultaneous voting, Feddersen and Pesendorfer (1998) consider the possibility that jurors vote strategically, rather than sincerely reflecting their individual information. This results in the counterintuitive result that a jury is mor...
Wafer map defect pattern classification supports quality monitoring in semiconductor manufacturing, but public benchmark datasets such as WM-811K exhibit extreme class imbalance, where majority classes can dominate standard metrics. This study aims t...
Whilst investigating student performance in design and arithmetic tasks, as well as during exams, electrodermal activity (EDA)-based sensors have been used in attempts to understand cognitive function and cognitive load. Limitations in the employed a...
Interest changes complicate sequential recommendation when interaction histories are combined with item content. We evaluate MM-DRLSR on four public Amazon and Yelp benchmarks. The model integrates category-overlap drift supervision, history-derived...
To train skilled unmanned combat aerial vehicle (UCAV) operators, it is important to establish a real-time training environment where an enemy appropriately responds to the action performed by a trainee. This can be addressed by constructing the infe...
To address the challenges of infrared dim small target detection under sky cloud backgrounds on edge devices, this study proposes a lightweight sequential-differential-frame-based network (LSDF-Net) with optimization and deployment on the heterogeneo...
In order to better understand transmission dynamics and appropriately target control and preventive measures, studies have aimed to identify who-infected-whom in actual outbreaks. Numerous reconstruction methods exist, each with their own assumptions...
When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as...
Experimental optimization with surrogate models has received much attention for its efficiency recently in predicting the responses of the experimental optimum. However, with the development of multi-fidelity experiments with surrogate models such as...
Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in f...
This study proposes a new condition diagnosis method for rotating machinery developed using least squares mapping (LSM) and a fuzzy neural network. The non-dimensional symptom parameters (NSPs) in the time domain are defined to reflect the features o...
This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this...
In image sensing, measurements such as an object’s position or contour are typically obtained by analyzing digitized images. This method is widely used due to its simplicity. However, relative motion or inaccurate focus can cause motion and def...
The Tsushima leopard cat (Prionailurus bengalensis euptilurus), an endangered feline endemic to Tsushima Island, Japan, faces critical threats due to its small and isolated population. Understanding its demographic history and genetic differentiation...
This paper introduces AIDE, an active inference-driven evaluation framework designed to provide a unified and theoretically grounded approach for analyzing sequential textual data. AIDE formulates the evaluation problem as variational inference in a...
The arbitrary-order hidden Markov model (
Probabilistic risk assessment (PRA) relies on manually constructed fault trees that cannot discover unknown root causes from observed failures. We present a causal knowledge graph approach for nuclear safety that addresses this limitation through bac...
This technical note applies active inference to the problem of learning goal-directed behaviour from scratch, namely, de novo learning. By de novo learning, we mean discovering, directly from observations, the structure and parameters of a discrete g...
A novel intelligent fault diagnosis method for motor roller bearings which operate under unsteady rotating speed and load is proposed in this paper. The pseudo Wigner-Ville distribution (PWVD) and the relative crossing information (RCI) methods are u...
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial becaus...
Large language models (LLMs) have traditionally relegated inference to remote servers, leaving mobile devices as thin clients. Recently, advances in mobile GPUs and NPUs have made on-device inference increasingly feasible, particularly for privacy-se...
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