A5101638376- Bayesian Modeling and Causal Inference
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
- Risk and Safety Analysis
- Bayesian Methods and Mixture Models
- Gene Regulatory Network Analysis
- Data Management and Algorithms
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Software Reliability and Analysis Research
- Multi-Criteria Decision Making
- Advanced Causal Inference Techniques
- Advanced Statistical Methods and Models
- Complex Systems and Decision Making
- Data Quality and Management
- Forecasting Techniques and Applications
- Computational Drug Discovery Methods
- Data-Driven Disease Surveillance
- Fault Detection and Control Systems
- Functional Brain Connectivity Studies
- Gaussian Processes and Bayesian Inference
- Radioactive contamination and transfer
- AI-based Problem Solving and Planning
- Game Theory and Applications
- Food Security and Health in Diverse Populations
University of Warwick
2015-2024
The Alan Turing Institute
2015-2024
Turing Institute
2017-2022
British Library
2018-2022
University of Iowa
2012
Electric Power Research Institute
2008
University of North Dakota
1997
JPMorgan Chase & Co (United States)
1992
Abstract Background Picoeukaryotes represent an important, yet poorly characterized component of marine phytoplankton. The recent genome availability for two species Ostreococcus and Micromonas has led to the emergence picophytoplankton comparative genomics. Sequencing revealed many unexpected features about structure several hypotheses on biology physiology. Despite accumulation genomic data, little is known gene expression in eukaryotic picophytoplankton. Results We have conducted a...
The rudiments of decision analysis. Decision trees. Utilities and rewards. Subjective probabilities their measurement. Influence diagrams, group decisions some practical problems in Bayesian statistics for Bayes estimation.
Summary The Bayesian Steady Forecasting model is generalized to a very wide class of processes other than the normal by defining time series on decision space. Examples such are presented including Beta-Binomial process, Poisson-Gamma process and Student-t sample distribution steady model. Simple updating relations given for most discussed.
In Escherichia coli, damage to DNA induces the expression of a set genes known collectively as SOS response. Part response includes that repair damage, but another part coordinates replication and septation prevent untimely cell division. The classic gene product inhibits division is SfiA (or SulA), which binds FtsZ prevents septum formation until has been repaired. However, pathway acts coordinate when sfiA, or sfi-dependent pathway, inoperative. Until recently, little was this alternative...
Background: Gaming techniques are increasingly recognized as effective methods for changing behavior and increasing user engagement with mobile phone apps. The rapid uptake of games provides an unprecedented opportunity to reach large numbers people influence a wide range health-related behaviors. However, digital interventions still nascent in the field health care, optimum gamified achieving change being investigated. There is currently lack worked methodologies that app developers care...
When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, decision maker (DM) must currently concern themselves with inference for parameter value minimising Kullback–Leibler (KL)-divergence between model and this process (Walker, 2013). However, has long been known KL-divergence places a large weight on correctly capturing tails of sample distribution. As result, DM required worry about robustness their tail...
SUMMARY Multiregression dynamic models are defined to preserve certain conditional independence structures over time across a multivariate series. They non-Gaussian and yet they can often be updated in closed form. The first two moments of their one-step-ahead forecast distribution easily calculated. Furthermore, built contain all the features univariate linear model promise more efficient identification causal series than has been possible past.
The search for a useful explanatory model based on Bayesian Network (BN) now has long and successful history. However, when the dependence structure between variables of problem is asymmetric then this cannot be captured by BN. Chain Event Graph (CEG) provides richer class models which incorporates these types structures as well retaining property that conclusions can easily read back to client. We demonstrate real health study how CEG leads us promising higher scoring further enables make...
A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in graphical way. One the advantages this that, contrast to many other Bayesian Networks, hypothesised relationships accommodate conditional conjugate inference. We demonstrate for first how straightforward it search over all possible connectivity networks with dynamically changing intensity transmission find Maximum Posteriori Probability (MAP) model within class....
There are a growing number of neuroimaging methods that model spatio-temporal patterns brain activity to allow more meaningful characterizations networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs multivariate with time-varying coefficients describe instantaneous relationships between nodes. A further benefit is networks may contain loops and large can be estimated. We use network simulations human resting-state fMRI (N = 500)...
Abstract The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead conventional tools, we apply a probabilistic model called the chain event graph (CEG) to represent failures processes deterioration system. CEG is derived an tree can flexibly unfolding asymmetric processes. For application, need define new class formal intervention call remedial causal effects maintenance. This fixes root causes failure...
Journal Article Kalman filtration of radiation monitoring data from atmospheric dispersion radioactive materials Get access Martin Drews, Drews *Corresponding author: bent.lauritzen@risoe.dk Search for other works by this author on: Oxford Academic PubMed Google Scholar Bent Lauritzen, Lauritzen Henrik Madsen, Madsen Jim Q. Smith Radiation Protection Dosimetry, Volume 111, Issue 3, 11 October 2004, Pages 257–269, https://doi.org/10.1093/rpd/nch339 Published: 20 July 2004 history Revision...
We provide a complete description of possible distributions consistent with any Gaussian latent tree model. This consists polynomial equations and inequalities involving covariances between the observed variables. Testing inequality constraints can be done using inverse Wishart distribution this leads to simple preliminary assessment tree-compatibility. To test equality we employ general techniques tetrad analyses. approach is effective even for small sample sizes easily adjusted either...
This paper considers inference of causal structure in a class graphical models called "conditional DAGs". These are directed acyclic graph (DAG) with two kinds variables, primary and secondary. The secondary variables used to aid estimation relationships between the variables. We give semantics for this model prove that, under certain assumptions, direction influence is identifiable from joint observational distribution A score-based approach developed using these consistency results...