BEGIN:VCALENDAR
VERSION:2.0
PRODID:Ludger using PHP/v0.1
X-PUBLISHED-TTL:PT15MIN
METHOD:PUBLISH
X-WR-CALNAME:Maths & Stats: Statistics Seminar
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10740@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20211210T150000Z
DTEND:20211210T160000Z
SUMMARY:Statistics Seminar: Identification of Underlying Partial Differential Equations from Noisy Data with Splines - Xiaoming Hao
DESCRIPTION:Abstract: We propose a two-stage method called Spline Assisted Partial Differential Equation involved Model Identification (SAPDEMI) to efficiently identify the underlying partial differential equation (PDE) models from the noisy data. In the first stage\, we employ the cubic spline to estimate the unobservable derivatives\, where some of them govern the underlying...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10740
LOCATION:https://www.smartsurvey.co.uk/s/MNW32H/
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10805@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220114T150000Z
DTEND:20220114T160000Z
SUMMARY:Statistics Seminar: Probabilistic energy forecasting: successes and challenges - Jethro Browell
DESCRIPTION:Abstract: Energy systems are evolving rapidly as they decarbonize\, consequences of which include an increasing dependence on weather and new consumer (and producer) behaviours. As a result\, all actors in the energy sector are more reliant than ever on short-term forecasts\, from the National Grid to me and (maybe) you. Furthermore\,...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10805
LOCATION:Zoom
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10751@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220128T150000Z
DTEND:20220128T160000Z
SUMMARY:Statistics Seminar: Computational Metabolomics as a game of Battleships - Vinny Davies (University of Glasgow)
DESCRIPTION:Abstract: Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Strategies for acquiring data in LC-MS/MS are however very limited and we usually only acquire around 30% of the data available to the detriment of follow-up experiments. In our recent work...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10751
LOCATION:https://www.smartsurvey.co.uk/s/MNW32H/
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10752@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220211T150000Z
DTEND:20220211T160000Z
SUMMARY:Statistics Seminar: Statistical methods for nowcasting daily hospital deaths from COVID-19 - Oliver Stoner
DESCRIPTION:Abstract: Delayed reporting is a significant problem for effective pandemic surveillance and decision-making. In the absence of timely data\, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. I will first explain four key sources of systematic and random variability in available data for...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10752
LOCATION:https://www.smartsurvey.co.uk/s/MNW32H/
SEQUENCE:3
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10803@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220225T150000Z
DTEND:20220225T160000Z
SUMMARY:Statistics Seminar: Explaining Artificial Intelligence: Contrastive Explanations for AI Black Boxes and What People Think of Them - Mark Keane (University College Dublin )
DESCRIPTION:Abstract: In recent years\, there has been a lot of excitement around the apparent success of Deep Learning in AI. There has also been a decent amount of skepticism around the issue of knowing what these models are actually doing\, when they are being successful. This has led to the emerging area of...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10803
LOCATION:TBA
SEQUENCE:5
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10807@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220311T150000Z
DTEND:20220311T160000Z
SUMMARY:Statistics Seminar: Going with flow: transport methods and neural networks for sequential Monte Carlo methods - Yunpeng Li (University of Surrey )
DESCRIPTION:Abstract: Sequential state estimation in non-linear and non-Gaussian state spaces has a wide range of applications in signal processing and statistics. One of the most effective non-linear filtering approaches\, particle filters a.k.a. sequential Monte Carlo methods\, suffer from weight degeneracy in high-dimensional filtering scenarios. A particular challenge for the deployment...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10807
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10804@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220325T130000Z
DTEND:20220325T140000Z
SUMMARY:Statistics Seminar: Generating Causal Explanations for Graph Neural Networks - Wanyu Lin (Hong Kong Polytechnic University)
DESCRIPTION:Abstract: These years\, we have witnessed the increasing attention of deep learning on graphs with graph neural networks (GNNs) from academia and industry. GNNs have exhibited superior performance across various disciplines\, such as healthcare systems\, financial systems\, and social information systems. These systems are typically required to make critical decisions\, such...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10804
LOCATION:Zoom
SEQUENCE:5
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10818@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220408T140000Z
DTEND:20220408T150000Z
SUMMARY:Statistics Seminar: High-performance importance sampling schemes for Bayesian inference - Víctor Elvira (University of Edinburgh)
DESCRIPTION:Abstract: Importance sampling (IS) is an elegant\, theoretically sound\, flexible\, and simple-to-understand methodology for approximation of moments of distributions in Bayesian inference (and beyond). The only requirement is the point-wise evaluation of the targeted distribution. The basic mechanism of IS consists of (a) drawing samples from simple proposal densities\, (b) weighting...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10818
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10852@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220429T140000Z
DTEND:20220429T150000Z
SUMMARY:Statistics Seminar: Spatial capture-recapture density estimation with latent\, partial\, and erroneous individual identity - Ben Augustine (USGS)
DESCRIPTION:Abstract: Capture-recapture study designs and statistical models are commonly used to estimate wildlife population parameters such as density\, survival\, and population growth rate\, which inform conservation and management decisions. One of the key assumptions of capture-recapture models is that all individuals are identified without error upon capture or detection\, and error...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10852
LOCATION:Zoom
SEQUENCE:5
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10819@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220506T140000Z
DTEND:20220506T150000Z
SUMMARY:Statistics Seminar: The All Possible Comparisons (APC)-criterion for the Analysis of Screening Experiments - Abu Zar Md Shafiullah (University of Dhaka )
DESCRIPTION:Abstract: An AIC-type model selection criterion\, called the APC-criterion (All Possible Comparisons criterion) is proposed for analyzing a variety of screening experiments. The primary focus is on the orthogonal 2-level designs including the Plackett-Burman designs (PBD) where the effects of k factors can be analyzed in only n = k +...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10819
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10863@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220520T100000Z
DTEND:20220520T110000Z
SUMMARY:Statistics Seminar: The tale of experimental designs - Emi Tanaka (Monash University )
DESCRIPTION:Abstract: Analysing experimental data is essential in helping us advance our knowledge in almost all scientific domains (e.g. ecology\, biology\, psychology\, etc)\, in addition to aiding data driven decisions in industry (e.g. drug development\, web design\, search algorithms\, etc). However\, as a precursor to any analysis\, the importance of the design...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10863
LOCATION:Zoom
SEQUENCE:3
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10866@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220601T140000Z
DTEND:20220601T150000Z
SUMMARY:Statistics Seminar: Fixing fixed-effects meta-analysis: some theoretical and practical advances - Kenneth Rice (University of Washington)
DESCRIPTION:Abstract: Meta-analysis is a common tool for synthesizing results of multiple studies\, for example combining clinical trial or genetic association signals. Despite being well-established\, some of its best-known methods are routinely misunderstood. Specifically\, “fixed effects” (in the plural) methods do not require that exact homogeneity is assumed\, and “random effects” methods...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10866
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10864@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220701T140000Z
DTEND:20220701T150000Z
SUMMARY:Statistics Seminar: COMBSS: Best Subset Selection via Continuous Optimization - Samuel Muller (Macquarie University)
DESCRIPTION:Abstract: Recent rapid developments in information technology have enabled the collection of high-dimensional complex data\, including in engineering\, economics\, finance\, biology\, and health sciences. High-dimensional means that the number of features is large and often far higher thanthe number of collected data samples. In many of these applications\, it is...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10864
LOCATION:Zoom
SEQUENCE:11
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10903@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220707T140000Z
DTEND:20220707T150000Z
SUMMARY:Statistics Seminar: Two applications of the variational form of Bayes theorem - Håvard Rue (KAUST)
DESCRIPTION:Abstract: In this talk I will discuss the variational form of Bayes theorem by Zellner (1988). This result gives a rationale for the variational (approximate) inference scheme\, which is not always that clear in modern presentations. I will discuss two applications of this results. First\, I will show how to do...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10903
LOCATION:Maths 311B
SEQUENCE:3
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10869@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220916T140000Z
DTEND:20220916T150000Z
SUMMARY:Statistics Seminar: Structured prior distributions for the covariance matrix in latent factor models - Sarah Heaps (Durham University)
DESCRIPTION:Abstract: Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a p x p covariance matrix into the sum of two components. Through a latent factor representation\, they can be interpreted as a diagonal matrix of idiosyncratic variances and a...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10869
LOCATION:Boyd Orr 420
SEQUENCE:6
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10908@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20220930T140000Z
DTEND:20220930T150000Z
SUMMARY:Statistics Seminar: Towards intelligent operation and maintenance in large-scale sustainable energy systems - Yingying Zhao (Fudan University )
DESCRIPTION:Abstract: Sustainable energy\, as an alternative to fuel energy\, has readily received concerns from society because of its merits of zero carbon emissions. Owing to the availability of sensor data\, operation and maintenance (O&M) of sustainable energy systems now becomes more intelligent. In particular\, data-driven anomaly detection approaches have gained growing...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10908
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10924@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221007T140000Z
DTEND:20221007T150000Z
SUMMARY:Statistics Seminar: Advanced Bayesian regression and dynamic network modelling - Abdul Salam (University of Groningen\, Netherlands)
DESCRIPTION:Abstract: The talk focuses on two well-known and widely applied statistical model classes\, namely\, the class of dynamic Bayesian network (DBN) models and the class of seemingly unrelated regression (SUR) models. For both model classes we have proposed methodological improvements and we have shown that the improvements can yield significantly better...\N\NNotes: Join Zoom Meetinghttps://uofglasgow.zoom.us/j/89895450683?pwd=N0xDejBocWVQenpnQ2hwQWE4YThOdz09 Meeting ID: 898 9545 0683Passcode: 874624\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10924
LOCATION:Zoom
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10907@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221014T120000Z
DTEND:20221014T130000Z
SUMMARY:Statistics Seminar: Helping biomedical scientists read the vast research literature using machine learning and natural language processing - Jake Lever (University of Glasgow)
DESCRIPTION:Abstract: Biomedical researchers face an overwhelming number of papers to read as their research becomes increasingly interdisciplinary. We must build automated methods to help them digest this vast knowledge and guide them to new research directions. Information extraction methods offer the opportunity to intelligently summarize the combined biomedical knowledge locked in...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10907
LOCATION:Maths 311B and Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10913@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221114T130000Z
DTEND:20221114T140000Z
SUMMARY:Statistics Seminar: Understanding building energy efficiency and tree preservation order by using administrative/emerging urban big data and deep learning - Qunshan Zhao (University of Glasgow)
DESCRIPTION:Abstract: Nowadays\, digital footprints data (geospatial data\, commercial and transactional data\, sensor and image data) have been widely generated and produced in our daily life. However\, how to effectively use these new forms of urban big data has remained unclear and needs further exploration. In this presentation I will introduce two pieces...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10913
LOCATION:Maths 311B
SEQUENCE:6
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10966@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221118T150000Z
DTEND:20221118T160000Z
SUMMARY:Statistics Seminar: A first approach to Fuzzy Multi-Objective Shortest Path Problems - Irene Marinas (University of Oviedo\, Spain)
DESCRIPTION:Abstract: One of the most studied classical optimization problems is the shortest path problem (SPP). The value of paths is normally measured in terms of a single attribute (cost\, duration\, time\, risk...) defined in each arc of the graph. However\, in many cases\, a single attribute is insufficient to...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10966
LOCATION:Maths 116
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10949@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221202T130000Z
DTEND:20221202T140000Z
SUMMARY:Statistics Seminar: Big weather\, small health (data) -- statistical models for understanding climate effects on mortality - Theo Economou (The Cyprus Institute)
DESCRIPTION:Abstract: Quantifying the effects of climate change on human health requires weather data as well as public health data. Arguably however\, there are more (publicly available) weather data than health data\, for various reasons but mostly due to the variability in how different countries manage their health data. Having too much...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10949
LOCATION:Maths 311B
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10909@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20221209T150000Z
DTEND:20221209T160000Z
SUMMARY:Statistics Seminar: Generative Quantile Regression with Variability Penalty - Ray Bai (University of South Carolina)
DESCRIPTION:Abstract: Quantile regression and conditional density estimation can often reveal structure that is missed by mean regression\, such as heterogeneous subpopulations (i.e. multimodality) and skewness. In this talk\, we introduce a deep learning generative model for simultaneous quantile regression called Penalized Generative Quantile Regression (PGQR). Our approach simultaneously generates samples from...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10909
LOCATION:Zoom
SEQUENCE:3
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10945@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20230113T150000Z
DTEND:20230113T160000Z
SUMMARY:Statistics Seminar: Prior Dependence in L1-regularized Bayesian Regression - Christopher Hans (Department of Statistics\, Ohio State University)
DESCRIPTION:Abstract: TBC\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10945
LOCATION:Zoom
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10912@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20230120T150000Z
DTEND:20230120T160000Z
SUMMARY:Statistics Seminar: TBA - Emma Simpson (University College London)
DESCRIPTION:More information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10912
LOCATION:Maths 311B
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10948@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20230217T150000Z
DTEND:20230217T160000Z
SUMMARY:Statistics Seminar: Distilling importance sampling for likelihood-free inference - Dennis Prangle (University of Bristol)
DESCRIPTION:Abstract: Likelihood-free inference involves inferring parameter values given observed data and a simulator model. The simulator is computer code taking the parameters\, performing stochastic calculations\, and outputting simulated data. In this work\, we view the simulator as a function whose inputs are (1) the parameters and (2) a vector of pseudo-random...\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10948
LOCATION:Zoom
SEQUENCE:4
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10960@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20230317T150000Z
DTEND:20230317T160000Z
SUMMARY:Statistics Seminar: TBA - BioSS
DESCRIPTION:More information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10960
LOCATION:Maths 311B
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
BEGIN:VEVENT
UID:GLW_UNI_MS_WEBEVT_10925@maths-stats.gla.ac.uk
DTSTAMP:19700101T000000Z
DTSTART:20230324T150000Z
DTEND:20230324T160000Z
SUMMARY:Statistics Seminar: Binscatter - Matias D. Cattaneo (Princeton University\, USA)
DESCRIPTION:Abstract: tbc\N\NMore information: http://www.gla.ac.uk/schools/mathematicsstatistics/events/details/?id=10925
LOCATION:Maths 311B
SEQUENCE:2
STATUS:CONFIRMED
CLASS:PUBLIC
END:VEVENT
END:VCALENDAR