A Bayesian network approach to explaining time series with changing structure

Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representa- tion and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset

Towards a dynamic view of genetic networks: A Kalman filtering framework for recovering temporally-rewiring stable networks from undersampled data

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. We refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth and cancer progression. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the networks estimation as a target tracking problem. Assuming linear dynamics, we formulate a constrained Kalman ltering framework, which recursively computes the minimum mean-square, sparse and stable estimate of the network connectivity at each time point. The sparsity constraint is enforced using the weighted l1-norm; and the stability constraint is incorporated using the Lyapounov stability condition. The proposed constrained Kalman lter is formulated to preserve the convex nature of the problem. The algorithm is applied to estimate the time-varying networks during the life cycle of the Drosophila Melanogaster (fruit fly)

Using dynamic Bayesian networks with hidden variables for change inference of the plankton community in the Archipelago Sea

Vuorovaikutukset planktonyhteisön sisällä ovat monimutkaisia ja näiden vuorovaikutusten realistinen mallintaminen on haaste ekosysteemitason mallinnuksessa. Tämän opinnäytetyön tavoitteena oli tutkia soveltuisivatko Bayes-verkot näiden vuorovaikutusten mallintamiseen. Työn toinen tavoite oli tutkia mahdollista muutosta ekosysteemitasolla. Tutkimuksessa käytettiin dynaamisia Bayes-verkkoja piilomuuttujilla ja tarkkailtiin, voisivatko muutokset planktonyhteisöjen rakenteessa heijastua laajempiin muutoksiin akvaattisissa ekosysteemeissä. Mallien tarkkuuden ja suorituskyvyn vertailua varten luotiin kaksi Bayes-ravintoverkkoa, joissa havaintojen väliset kausaaliset linkit eroavat toisistaan. Yksinkertaisempi rakenne, joka perustuu Markovin piilomalliin, suoriutui paremmin ja havaitsi piilomuuttujassa selkeän trendin. Tämä trendi aikasarjassa viittaa siihen, että tarkasteltavien muuttujien väliset suhteet ovat muuttuneet tutkimusjakson aikana. Analyyseissä käytetty planktonaineisto oli kerätty vuosien 1991 ja 2016 välillä Saaristomeren tutkimusasemalta ja mallin tuloksia arvioitiin yhdessä näytteistä kerätyn aineiston kanssa. Kasviplanktonin kokonaisbiomassa näytteissä kasvoi tutkimusajanjakson aikana, ja vastaavasti samaan aikaan eläinplanktonin kokonaisbiomassa näytteissä väheni. Bayes-verkko huomioi muutokset tarkastelluissa muuttujissa ja samaan aikaan maksimoi piilomuuttujan sopivuuden. Havaittu muutos piilomuuttujassa viittaa siis siihen, että jotkin muuttujat, jotka eivät ole havaittavissa, vaikuttavat molempien planktonyhteisöjen rakenteeseen. Havaittu kehityssuunta piilomuuttujassa saattaa viitata Saaristomeren rehevöitymiseen tutkimusajanjakson aikana, mutta tarkempien syiden selvittäminen vaatii lisätutkimuksia. Tämän opinnäytetyön perusteella piilomuuttujilla varustettu dynaaminen Bayes-verkko on lupaava menetelmä planktonyhteisön mallintamiseen.The interactions within plankton communities are complex, and realistic modelling of these interactions create a challenge in large-scale environmental models. The objective of this thesis was to evaluate whether Bayesian networks could be a suitable method in the modelling of these communities. Besides observing the interactions between different groups within phyto- and zooplankton communities, another goal was to focus on the potential change on the ecosystem level. To achieve this, dynamic Bayesian networks with hidden variables were used to observe whether structural changes in plankton communities could reveal larger trends in the aquatic ecosystem. To compare performance and accuracy of the model, two Bayesian food webs with differing causal links between observations were built. Of the two models, the simpler construct utilizing hidden Markov model fared better, and a clear trend was detected in the hidden variable. This trend in the time series signify that the relationships between the observed variables have changed during the study period. The plankton data set was collected from the Archipelago Sea between 1991 and 2016 and the results from the model were further analyzed alongside with this observational plankton data. In the samples the total biomass of phytoplankton grew throughout the study period, whereas at the same time the total biomass of zooplankton declined. As the Bayesian network considers the observable variables while maximizing the fit of the hidden variable, the observed trend in the hidden variable indicate that some unobservable variables are affecting both phyto- and zooplankton communities. This clear trend detected by the hidden variable might be related to a trend of increasing eutrophication in the study area, but to better understand the drivers causing this change further research is needed. Besides detecting underlying trends, the dynamic Bayesian networks are a promising method to study the interactions within plankton communities

Hidden variables in a Dynamic Bayesian Network identify ecosystem level change

EU; The Academy of Finland; Projektträger Jülich (PtJ); Germany; The State Education Development Agency of Latvia; The National Centre for Research and Development, Poland; The Swedish Research Council Formas; BalticEye Stockholm University; foundation BalticSea202

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Bayesian network modelling provides spatial and temporal understanding of ecosystem dynamics within shallow shelf seas

Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is needed to predict ecosystems responses to such changes. This study uses Bayesian techniques to find the data-driven estimates of interactions among a set of physical and biological variables and a human pressure within the last 30 years in a well-studied shallow sea (North Sea, UK) with four contrasting regions and their associated ecosystems. A hidden variable is incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following natural or anthropogenic disturbance. Data-driven estimates of interactions were identified, highlighting physical (e.g. bottom temperature, potential energy anomaly) and biological variables (e.g. sandeel larvae, net primary production) to be strong indicators of ecosystem change. There was consistency in the physical and biological variables, identified as good indicators in three of the regions, however the shallower region (with depths < 50 m, that is targeted for static offshore wind developments) was the most dissimilar. The use of contrasting regions provided useful insights on responses linked to ecosystem disturbances and identified the top predators as better indicators for each region, with the harbour porpoise being a particularly valuable indicator of ecosystem change across most regions. Another important finding was the dramatic changes in the strength of many interactions over time. This suggests that physical and biological indicators should only be used with additional temporal information, as changes in strength led to the identification of two potentially significant periods of ecosystem change (after 2005 and after 2010), linked to physical pressures (e.g. cold-water anomalies, seen in bottom temperatures; salinity changes, seen in the potential energy anomaly) and primary production changes. The hidden variable also modelled a change in the early 2000s for all the regions and identified maximum chlorophyll-a and sea surface temperature as some of the better indicators of these ecosystem changes