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BEGIN:VEVENT
DTSTAMP:20260427T211134Z
DTSTART;VALUE=DATE-TIME:20190131T140000
DTEND;VALUE=DATE-TIME:20190131T160000
SUMMARY:CRiSM Seminar
TZID:Europe/London
UID:20190131-8a17841b65af76200165d76631630185@warwick.ac.uk
CREATED:20190117T124201Z
DESCRIPTION:Professor Paul Fearnhead\, Lancaster University - 14:00-1500 
 Efficient Approaches to Changepoint Problems with Dependence Across Segm
 ents Changepoint detection is an increasingly important problem across a
  range of applications. It is most commonly encountered when analysing t
 ime-series data\, where changepoints correspond to points in time where 
 some feature of the data\, for example its mean\, changes abruptly. Ofte
 n there are important computational constraints when analysing such data
 \, with the number of data sequences and their lengths meaning that only
  very efficient methods for detecting changepoints are practically feasi
 ble. A natural way of estimating the number and location of changepoints
  is to minimise a cost that trades-off a measure of fit to the data with
  the number of changepoints fitted. There are now some efficient algorit
 hms that can exactly solve the resulting optimisation problem\, but they
  are only applicable in situations where there is no dependence of the m
 ean of the data across segments. Using such methods can lead to a loss o
 f statistical efficiency in situations where e.g. it is known that the c
 hange in mean must be positive. This talk will present a new class of ef
 ficient algorithms that can exactly minimise our cost whilst imposing ce
 rtain constraints on the relationship of the mean before and after a cha
 nge. These algorithms have links to recursions that are seen for discret
 e-state hidden Markov Models\, and within sequential Monte Carlo. We dem
 onstrate the usefulness of these algorithms on problems such as detectin
 g spikes in calcium imaging data. Our algorithm can analyse data of leng
 th 100\,000 in less than a second\, and has been used by the Allen Brain
  Institute to analyse the spike patterns of over 60\,000 neurons. (This 
 is joint work with Toby Hocking\, Sean Jewell\, Guillem Rigaill and Dani
 ela Witten.) Dr. Sandipan Roy\, Department of Mathematical Science\, Uni
 versity of Bath (15:00-16:00) Network Heterogeneity and Strength of Conn
 ections Abstract: Detecting strength of connection in a network is a fun
 damental problem in understanding the relationship among individuals. Of
 ten it is more important to understand how strongly the two individuals 
 are connected rather than the mere presence/absence of the edge. This pa
 per introduces a new concept of strength of connection in a network thro
 ugh a nonparameteric object called ā€œGrafieldā€. ā€œGrafieldā€ is a piece-wis
 e constant bi-variate kernel function that compactly represents the affi
 nity or strength of ties (or interactions) between every pair of vertice
 s in the graph. We estimate the ā€œGrafieldā€ function through a spectral a
 nalysis of the Laplacian matrix followed by a hard thresholding (Gavish 
 & Donoho\, 2014) of the singular values. Our estimation methodology is v
 alid for asymmetric directed network also. As a by product we get an eff
 icient procedure for edge probability matrix estimation as well. We vali
 date our proposed approach with several synthetic experiments and compar
 e with existing algorithms for edge probability matrix estimation. We al
 so apply our proposed approach to three real datasets- understanding the
  strength of connection in (a) a social messaging network\, (b) a networ
 k of political parties in US senate and (c) a neural network of neurons 
 and synapses in C. elegans\, a type of worm.
LOCATION:MSB2.23
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20190117T124201Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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