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International projects
| ANTICIPATE – ANTICIPATE: extended-range multi-hazard predictions and early warnings | |
| ANTICIPATE: dlhodobé (rozšírené) predpovede viacerých typov rizík a včasné varovania | |
| Program: | COST |
| Duration: | 29.10.2025 – 28.10.2029 |
| Project leader: | Ing. Košta Radoslav |
| Annotation: | Operational extreme weather forecasts and early warnings are generally limited to timescales of up to around 10 days and to predicting single events, such as flooding or a heatwave. However, a new generation of experimental ‘extended-range’ weather predictions that extend up to 46 days have been developed over the last decade by the world’s leading meteorological centres. A key motivation of exploring this prediction timescale is to bridge the gap between timescales, incorporate the latest ‘multi-hazard’ approaches, and improve early warnings and anticipatory actions. Currently, however, the extended-range prediction and the multi-hazard communities are largely disconnected. To date, there has been no coordinated effort to build a network that connects these disciplines and communities towards the development of operational systems. However, it is essential that these communities come together to explore windows of opportunity and instigate a step-change in the way forecasts are designed, produced and used. To address this challenge, ANTICIPATE will create the first pan-European network focused on extended-range multi-hazard predictions and warnings. ANTICIPATE will bring together existing but largely disconnected disciplines, operational practitioners and stakeholders (including extreme weather forecasting, extended-range prediction and climate dynamics, disaster risk reduction, multi-hazards, and communications) to drive forward advancements in the science, training, communication and application that will support next generation of effective early warnings that enable preparedness and action across hazards and forecasting lead times. ANTICIPATE will provide vital leadership in multi-hazard predictions and warnings, address gaps and challenges, and educate the next generation of forecasters and communicators for societal benefit. |
| Project website: | https://www.cost.eu/actions/CA24144/ |
| MEDUSSE – Seasonal-to-decadal climate predictability in the Mediterranean: process understanding and services | |
| Sezónna až dekádová predpovedateľnosť klímy v Stredomorí: pochopenie procesov a implementácie | |
| Program: | COST |
| Duration: | 8.10.2024 – 7.10.2028 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | Climate forecasting has enormous potential influence in different socio-economic sectors, such as agriculture, health, water management, and energy. Actionable climate information is particularly relevant at seasonal-to-decadal timescales, where predictability is linked to slow fluctuations of the system such as those in the ocean, sea-ice and land-surface, thus bridging weather/sub-seasonal predictions (mainly relying on atmospheric initial condition) with future projections (mainly based on atmospheric radiative forcing). Seasonal-to-decadal climate forecasting has progressed considerably in recent years, but prediction skill over the Mediterranean is still limited. Better understanding the drivers of regional climate anomalies as well as exploring untapped sources of predictability constitute a much-needed and timely effort.Climate variability and change pose significant challenges to society worldwide. As a result, there is a growing demand to develop improved climate information products and outlooks to help decision making and sustainable development. This is particularly critical in the Mediterranean, a region sensible to natural hazards (e.g. droughts, floods) and vulnerable to climate stress (i.e. global warming). Such an improvement can only be achieved by coordinating efforts of research groups with different expertise and trans-disciplinary. In this Action, both the scientific challenge and societal challenge will be addressed by establishing a network of experts on climate variability, predictability, prediction and application. The Action will provide support to increase awareness and capability, and guidance to suitably evolve climate knowledge into services. Specific objectives include cross-cutting training and collaboration, empowering national hydro-meteorological agencies, and fostering a continuous communication between climate researchers and stakeholders. |
| DYNALIFE – Information, Coding, and Biological Function: the Dynamics of Life | |
| Informácia, kódovanie a biologická funkcia: Dynamika života | |
| Program: | COST |
| Duration: | 19.9.2022 – 18.9.2026 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | In the mid-twentieth century two new scientific disciplines emerged forcefully: molecular biology and information-communication theory. At the beginning cross-fertilisation was so deep that the term genetic code was universally accepted for describing the meaning of triplets of mRNA (codons) as amino acids.However, today, such synergy has not take advantage of the vertiginous advances in the two disciplines and presents more challenges than answers. These challenges are not only of great theoretical relevance but also represent unavoidable milestones for next generation biology: from personalized genetic therapy and diagnosis, to artificial life, to the production of biologically active proteins. Moreover, the matter is intimately connected to a paradigm shift needed in theoretical biology, pioneered long time ago in Europe, and that requires combined contributions from disciplines well outside the biological realm. The use of information as a conceptual metaphor needs to be turned into quantitative and predictive models that can be tested empirically and integrated in a unified view. The successful achievement of these tasks requires a wide multidisciplinary approach, and Europe is uniquely placed to construct a world leading network to address such an endeavour. The aim of this Action is to connect involved research groups throughout Europe into a strong network that promotes innovative and high-impact multi and inter-disciplinary research and, at the same time, to develop a strong dissemination activity aimed at breaking the communication barriers between disciplines, at forming young researchers, and at bringing the field closer to a broad general audience. |
| STOCHASTICA – Stochastic Differential Equations: Computation, Inference, Applications | |
| Stochastické diferenciálne rovnice: výpočty, inferencia, aplikácie | |
| Program: | COST |
| Duration: | 26.9.2025 – 25.9.2029 |
| Project leader: | MSc. Krakovská Hana |
| Annotation: | Stochastic differential equations (SDEs) are used to model phenomena under the influence of random noise and uncertainty and are useful in an extraordinary range of applications. In health, SDE models of tumour growth can help medical practitioners design interventions. In clean energy, they can model airflow around wind turbine blades, and enable multiscale modelling of entire wind farms and energy grids by representing small scale effects as noise. In computing, SDEs can be used to develop training algorithms for deep learning algorithms.The development and effective deployment of stochastic models requires input from a broad range of specialist experts: applied modellers, theoretical mathematicians, numerical analysts, and statisticians, all guided by the needs of stakeholders in academia and industry. However, in the current European research landscape, there is no large scale framework enabling these communities to interact, and opportunities for goal-driven research progress that is informed by all relevant expertise are being lost.Under the umbrella of computational stochastics, STOCHASTICA will bring together members of all of these communities to create a network of researchers with common goals informed by academic and industry partners. The work of the Action will generate a computational toolbox including a database of test problems, implementation guidance, and accessible descriptions of mathematical quality that empower non-specialist experts to make appropriate and routine use of stochastic models in applications such as natural resource management, renewable energy transmission, medical and public health applications including epidemiology and models of tumour growth. |
| Project website: | https://www.ucc.ie/en/stochastica/ |
| DONUT – European Doctoral Network for Neural Prostheses and Brain Research | |
| Európska doktorandská sieť pre neurálne protézy a výskum mozgu | |
| Program: | Horizont Európa |
| Duration: | 1.1.2024 – 31.12.2027 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | DONUT, European Doctoral Network for Neural Prostheses and Brain Research has the mission to provide a multidisciplinary and inter-sectoral network for young talented researchers. The ambition of the project is to serve as a springboard for the expansion of EU partners into the fast-developing Brain-Computer Interface (BCI) technology and connected scientific disciplines. The DN will leverage the complementary expertise of 7 academic beneficiaries and 8 associated partners from 8 EU countries, to guide its 10 doctoral candidates (DCs) to address and solve deep problems in brain research, development of different BCI applications and systems with the latest technological advancements.The proposed DN will integrate existing research in BCI systems to make it more user-friendly, suitable for different types of potential end-users and for modern medical diagnostics. The DN would also provide excellent opportunities for career development of young researchers under the umbrella of German doctorate graduate school PK NRW (Graduate School for Applied Research in North Rhine-Westphalia, with over 180 participating professors), regularly offering specialised trainings and courses including Scientific Research Writing, Academic Presentation Skills, etc. Early scientific independence is one of key goals of training programmes.It is the ambition of DONUT to build a strong and lasting network not only between the DCs but also between the participating beneficiaries and associated partners. DONUT researchers will benefit of a dense network of contacts with scientists acquired during network-wide training events, to improve their career prospects in the European and worldwide innovation sector, having the opportunity to become scientists employable in both the industrial and academic sectors. The participation of 7 industrial participants in research and training programmes will guarantee extensive inter-sectoral experience for the trainees and maximise the impact. Project Partners:Rhine-Waal University of Applied Sciences (HSRW), GermanyRadboud University (RU), NetherlandsKatholieke Universiteit Leuven (“KU Leuven”) (KUL), BelgiumUniversidad Miguel Hernández de Elche (UMH), SpainAarhus University (AU), DenmarkKauno technologijos universitetas (KTU), LithuaniaInstitute of Measurement Science, Slovak Academy of Sciences (IMSAV), Slovakia |
| Precision Neuromodulation for Chronic Pain: Integrating Functional MRI and Focused Ultrasound for Personalised Treatment | |
| Presná neuromodulácia chronickej bolesti: Integrácia funkčnej magnetickej rezonancie a fokusovaného ultrazvuku pre personalizovanú liečbu, skratka „NeuroPain“ | |
| Program: | ERANET |
| Duration: | 1.1.2026 – 31.8.2028 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
National projects
| CARE-BCI – Cooperative AI-enhanced BCI-HMD rehabilitation for post-stroke recovery | |
| Kooperatívna AI BCI-HMD rehabilitácia pre pacientov po cievnej mozgovej príhode | |
| Program: | APVV |
| Duration: | 1.9.2026 – 31.8.2030 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | This project aims to advance post-stroke neurorehabilitation through the development of an artificial intelligence (AI)-enhanced, collaborative brain–computer interface (BCI) system integrated with immersive head-mounted display (HMD)–based virtual reality (VR). AI serves as a central enabling component, supporting adaptive neural decoding, cognitive-state monitoring, and data-driven optimization of rehabilitation protocols. A key focus is on the creation ofcooperative, shared-action rehabilitation environments, in which the patient and therapist jointly perform the same task in real time. This combination of AI-driven adaptation and shared-action cooperation moves beyond isolated task execution toward socially interactive, coordinated motor rehabilitation with high ecological validity.The approach extends the state of the art by employing AI for adaptive neural decoding, cognitive-state monitoring, and longitudinal meta-analysis of rehabilitation trajectories. Active BCI components use personalized models to decode motor imagery under inter- and intra-subject variability, while passive BCI continuously monitors cognitive workload, mentalfatigue, and engagement. An exploratory component investigates the feasibility of an AI-assisted therapeutic agent capable of partially supporting therapist actions within immersive, cooperative VR environments, while preserving safety, interpretability, and clinical oversight.The ambition is to establish a scalable and personalized neurorehabilitation framework that enhances therapeutic efficacy, strengthens patient–therapist interaction through shared-action VR tasks, and reduces therapist workload. By integrating active and passive BCI, cooperative VR, and explainable AI within a single coherent system, the project aims to generatenew scientific insights into rehabilitation dynamics and provide a clinically relevant pathway toward accessible, data-driven post-stroke rehabilitation in clinical and home-based settings. |
| CAUSMET – Methods and algorithms for causal analysis and quantification of measurement uncertainty | |
| Názov projektu Metódy a algoritmy kauzálnej analýzy a kvantifikácie neistôt meraní | |
| Program: | APVV |
| Duration: | 1.9.2026 – 31.12.2029 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The project develops advanced methods and algorithms for causal analysis of stochastic and deterministic processes and for quantifying measurement uncertainties. It addresses methodological challenges in the analysis of time series and dynamical data, where correlation alone is insufficient to reveal the mechanisms governing system behavior. Manyapplications, therefore, require identifying causal relations between variables while reliably characterizing uncertainties arising from measurement processes, noise, and incomplete observations.The project will develop classical and modern approaches to causal analysis of time series based on probabilistic and statistical modeling, and integrate them with algorithms enabling statistical inference and prediction in the presence of randomness, measurement errors, and uncertainty.Modern applications in physical, biomedical, economic, environmental, and linguistic measurements, as well as in the social sciences (education, psychology), generate large and complex datasets with intricate dependence structures and temporal dynamics. A significant project component is hence the study of stochastic dynamical models, including diffusion processes, as a natural framework for modeling random dynamics observed via measurement time series. When modeling complex temporal or spatio-temporal data using kriging, causal structure will serve as a key starting point.The project also advances uncertainty methods for quantifying measurement uncertainties in line with modern metrology and aims to establish a unified methodological framework combining causal analysis, dynamical modeling, and statistical inference and forecasting. Interdisciplinary collaboration among the Institute of Measurement Science of the SAS, theMathematical Institute of the SAS, and the Faculty of Science of P. J. Šafárik University creates favorable conditions for the development of new theoretical results, efficient algorithms, and their applications. |
| MRCartilage – Automatic data evaluation tool from the longitudinal quantitative MRI studies of articular cartilage | |
| Automatický softvérový nástroj na výhodnocovanie kvantitatívnych MRI štúdií artikulárných chrupaviek v čase | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Dr. Szomolányi Pavol, (PhD.) |
| Annotation: | The aim of the project is to design a comprehensive tool for automatic evaluation of human articular cartilage data from quantitative MRI. Data obtained from the Osteoarthritis Initiative database, and measured at Institute of Measurement Science and Medical University of Vienna will be segmented using an automated segmentation tool based on convolutional neural networks. The annotated data will then be registered on quantitative MRI data that will be available from the database (T2 and T1rho mapping, gagCEST, sodium MR) using automated or semiautomated tools developed within this project. The data obtained will be evaluated at multiple time points according to MR measurements that will be available. In addition to quantitative MR data, this will include volumetric data, cartilage thickness, and texture analysis of quantitative maps. Patient evaluation will be based on risk factor groups (transverse ligament rupture, meniscus rupture and menisectomy). The expected number of patients is approximately 4000 divided into individual groups in the ratio 40/30/30. The output of the project will be a compiled version of an automatic cartilage evaluation tool that will be available in a public source (such as website of Institute of Measurement). |
| Innovative approaches to uncovering relationships and interactions within multivariate time measurements | |
| Inovatívne prístupy k odhaľovaniu vzťahov a interakcií v rámci multivariátnych časových meraní | |
| Program: | VEGA |
| Duration: | 1.1.2026 – 31.12.2029 |
| Project leader: | RNDr. Krakovská Anna, CSc. |
| Annotation: | The project focuses on developing and applying methods for analyzing relationships between simultaneouslymeasured processes. After experience with bivariate causal detection, we now target multivariate cases, oftenmodelled as dynamical networks with time series at their nodes.We investigate Granger causality for autoregressive (AR) models and search for connections in reconstructedstate spaces when deterministic dynamics prevail. Transfer entropy, a strong representative of causal methods,will also be explored, including proposed modifications using alternative entropy measures. We also examine thepotential of machine learning methods in causal analysis.Expected outcomes include computational tools for more reliable detection of causal links and synchronisation,and for improved modelling, forecasting, and classification of the studied processes.The proposed methods will be validated on simulated data and applied to real measurements, such as effectivebrain connectivity and climate observations. |
| Innovations in the Transfer Entropy Method: Implementing Alternative Entropic Measures for More Robust Causal Inference | |
| Inovácie v metóde prenosovej entropie: Implementácia alternatívnych entropických mier pre robustnejšiu kauzálnu inferenciu | |
| Program: | Návratová projektová schéma |
| Duration: | 1.7.2025 – 30.6.2026 |
| Project leader: | Mgr. Mezeiová Kristína, PhD. |
| Annotation: | The aim of the project is to explore the use of alternative entropy measures, such as Rényi entropy, Tsallis entropy, and permutation entropy, in the transfer entropy method to enhance the accuracy, robustness, and computational efficiency of causal analysis in complex systems. The project will focus on the software implementation of appropriately modified causal algorithms, their testing on synthetic and real-world data, and the identification of areas where the proposed innovations provide significant advantages. |
| cBCI-VR – Collaborative BCI post-stroke neurorehabilitation using a patient-therapist interactive VR environment | |
| Pacient-terapeut kolaboratívna BCI-VR neurorehabilitácia po cievnej mozgovej príhode | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2024 – 31.8.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | A growing body of evidence suggests that integrated brain-computer interface (BCI) technologies and virtual reality (VR) environments provide a flexible platform for a range of neurorehabilitation therapies, including significant motor recovery and cognitive-behavioral therapy following stroke. When a subject is immersed in such an environment, their perceptual level of social interaction is often impaired due to a suboptimal interface quality that lacks the social aspect of human interactions. The project proposes a user-friendly intelligent BCI system with a suitable VR environment in which both patient and therapist interact through their person-specific avatar representations. On the one hand, the patient voluntarily and at his/her own pace controls his/her activity in the environment and interacts with the therapist through a BCI-driven mental imagery process. On the other hand, the therapist\’s unrestricted motor and communication skills allow for full control of the environment. Thus, the VR environment can be flexibly modified by the therapist, allowing for the creation and selection of different occupational therapy scenarios according to the patient\’s recovery needs, mental states, and immediate reactions. |
| TInVR – Trustworthy human–robot and therapist–patient interaction in virtual reality | |
| Dôveryhodná interakcia človek–robot a terapeut–pacient vo virtuálnej realite | |
| Program: | APVV |
| Duration: | 1.7.2022 – 30.6.2026 |
| Project leader: | Ing. Mgr. Rosipal Roman, DrSc. |
| Annotation: | We aim to study specific forms of social interaction using state-of-the-art technology – virtual reality (VR) which is motivated by its known benefits. The project has two main parts, human–robot interaction (HRI) and therapist–patient interaction (TPI). The interactions are enabled using head-mounted displays and controllers allowing the human to act in VR. We propose two research avenues going beyond the state-of-the-art in respective contexts. In HRI, we will develop scenarios allowing the humanoid robot to learn, understand and imitate human motor actions using flexible feedback. Next, we develop scenarios for testing and validating human trust in robot behavior based on multimodal signals. We will also investigate physical interaction with a humanoid robot NICO. In TPI with stroke patients, we develop a series of VR-based occupational therapy procedures for motor and cognitive impairment neurorehabilitation using an active and passive brain-computer interface, and we will validate these procedures. We expect observations from HRI experiments to be exploited in TPI. The proposed project is highly multidisciplinary, combining knowledge and research methods from psychology, social cognition, robotics, machine learning and neuroscience. We expect to identify features and mechanisms leading to trustworthy processes with a human in the loop, as a precondition of success, be it a collaborative task or treatment in VR. |
| EDABSS – EEG data analysis by blind source separation methods | |
| Analýza EEG signálu pomocou metód hľadania skrytých zdrojov | |
| Program: | Plán obnovy EÚ |
| Duration: | 1.9.2024 – 31.8.2026 |
| Project leader: | Mgr. Rošťáková Zuzana, PhD. |
| Annotation: | Blind source separation (BSS) approaches are unsupervised machine learning methods focused on the detection of hidden, directly unobservable (latent) structure of real-world data. They play a crucial role in image processing, medical imaging, and music. The proposed project focuses mainly on human electroencephalogram (EEG), for which BSS is beneficial when detecting the narrowband brain oscillations representing brain processes either in health or disease. Two-dimensional BSS methods like principal or independent component analysis are easily applicable and understandable for a broader medical and neurophysiological community. However, the estimated latent component properties are usually incompatible with the real electrophysiological signal character. Consequently, they miss their neurophysiological interpretation. Tensor decomposition is a complex but more flexible mathematical procedure that allows adapting the model structure and constraints to the solution to mimic real-world signal characteristics. The proposed project focuses on tensor decomposition as a tool for i) EEG preprocessing, artefact detection and removal, ii) EEG latent structure analysis using a nonnegative tensor decomposition with block structure allowing to model various relationships between latent components, and iii) post-decomposition analysis of latent component dynamic properties. Obtaining comprehensive information about EEG latent structure and developing novel, user-friendly algorithms is crucial for better understanding brain processes and new methods for treating neurophysiological diseases and disorders. |
| Effects of low-frequency and pulsed electromagnetic fields at a cellular level | |
| Účinky nízkofrekvenčných a pulzných elektromagnetických polí na bunkovej úrovni | |
| Program: | VEGA |
| Duration: | 1.1.2025 – 31.12.2028 |
| Project leader: | Mgr. Teplan Michal, PhD. |
| Annotation: | Although there is ongoing interest in the adverse and beneficial effects of electromagnetic fields (EMF), a clearexplanation of EMF\’s influence on living structures is lacking. To investigate low-frequency (LF) magnetic fields(MF), we will enhance our experimental platform to test their possible inhibitory or stimulatory effects based onfrequency and magnetic flux density parameters. As a model organism yeast strain Saccharomyces cerevisiaewill be used. Its response to time-harmonic and pulsed MF will be studied by measuring cell growth curve usingturbidimetry, impedance spectroscopy and microscopy. Moreover, the ion parametric resonance interactionmodel will be verified for biogenic ions and the magnitude of the ambient static geomagnetic field. The importanceof this area of research lies in exploring physical methods for manipulating biological structures, with potentialbenefits for biotechnology and medical treatment. |
| VERISCAN – Metrological framework for the verification of dynamic 3D scanning systems according to ISO GPS in digital manufacturing | |
| Metrologický rámec verifikácie dynamických 3D skenovacích systémov podľa ISO GPS v podmienkach digitálnej výroby | |
| Program: | APVV |
| Duration: | 1.9.2026 – 31.8.2029 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | The project addresses the lack of a comprehensive methodological framework for the verification of handheld 3D scanning systems. Despite their massive implementation in digital manufacturing (Industry 4.0/5.0), their metrological assurance lags behind technical hardware capabilities. The core scientific challenge is the missing link between the variable nature of handheld scanning (operator influence, trajectory, strategy) and the strict requirements of the Geometrical ProductSpecifications (ISO GPS) system.The objective is to research and develop a metrological framework that transforms handheld 3D scanning from avisualization tool into a full-fledged system for product conformity decision-making. The project focuses on developing specialized reference artifacts with complex geometry designed for dynamic optical systems. It uniquely combines the technological expertise of UNIZA in digital quality control with the fundamental metrological competencies of the Institute of Measurement SAS in calibration and uncertainty estimation (GUM).The original contribution is an ISO GPS-oriented verification methodology that systematically integrates dynamic measurement uncertainty sources into final conformity assessment. The outputs include a physical reference artifact with SI traceability and verified procedures for the automotive and machinery industries. The project directly supports digital manufacturing chains by enhancing production quality and reducing non-conformance costs through metrologically correctvalidation of complex components. |
| Characteristic function-based goodness-of-fit test for fuzzy data with application to climate analysis | |
| Testy dobrej zhody založené na charakteristickej funkcii pre neurčité údaje s aplikáciou na analýzu klimatických dát | |
| Program: | APVV |
| Duration: | 1.1.2026 – 31.8.2028 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | Modern research faces growing data uncertainty from measurement errors, gaps, and subjective assessments. Traditional statistical methods, assuming precise data, often fail under such conditions. Fuzzy data, which capture vagueness and imprecision, offer a natural framework, yet robust statistical tools for them remain scarce. This interdisciplinary project — combining probability and mathematical statistics, applied mathematics, and measurement science — aims to develop a goodness-of-fit test based on characteristic functions for fuzzy and interval-valued data. This novel methodology addresses both theoretical and applied challenges, with a focus on climate analysis. Objectives include: (1) Developing theoretical and empirical characteristic functions for fuzzy data, defining distance measures, formulating the test, and deriving its statistical properties. (2) Designing and implementing efficient algorithms in R, MATLAB, or Python. (3) Evaluating performance through simulations and benchmarking against existing methods. (4) Applying the method to real climate datasets (e.g., temperature, rainfall) to demonstrate its relevance under uncertainty. The methodology leverages the uniqueness and computational benefits of characteristic functions, extended to fuzzy settings. The project innovatively integrates characteristic functions and fuzzy theory for hypothesis testing, providing a statistically rigorous yet practical approach to imprecise data analysis. Expected outcomes include: a new statistical test, open-source software, simulation and benchmark studies, case studies on climate data, and preparation of a publication in leading journal. This bilateral project brings together expertise in fuzzy theory (University of Montenegro) and measurement science (Institute of Measurement Science of the Slovak Academy of Sciences). |
| Theoretical properties and applications of special families of probability distributions | |
| Teoretické vlastnosti a aplikácie špeciálnych tried rozdelení pravdepodobnosti | |
| Program: | VEGA |
| Duration: | 1.1.2024 – 31.12.2027 |
| Project leader: | Doc. RNDr. Witkovský Viktor, CSc. |
| Annotation: | In the project, problems related to probability distributions and their applications in mathematical modeling will be studied. We will analyze some classes of distributions (distributions generated by partial summations, the Schröter family) and study properties of distributions belonging to these classes. Issues related to calibration regression models will be addressed. New methods for solving multivariate statistical problems will be developed. These methods will be based on the calculation of exact probability distributions using the inverse transformation of the characteristic function of the distribution of the output variable. Entropy, another property of probability distributions, plays an important role in detecting causality in time series. The primary area of application is theuse of the distribution of test statistics in hypothesis testing. The new results obtained during the solution of the project will also be applied to mathematical modeling in metrology, linguistics and actuarial mathematics. |