BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Missouri State University/Calendar of Events//EN CALSCALE:GREGORIAN X-WR-TIMEZONE:America/Chicago BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT TZOFFSETFROM:-0600 TZOFFSETTO:-0500 DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0500 TZOFFSETTO:-0600 DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:0a79d9a6-cd38-4220-8c81-a3afd735500c.196844@calendar.missouristate.edu CREATED:20190321T210708Z LAST-MODIFIED:20190321T210708Z LOCATION:Cheek Hall 301 SUMMARY:Mathematics Colloquium: Some Remarks on the Construction of Design er Kernels and Their Applications DESCRIPTION:Positive definite reproducing kernels (or covariance kernels) play a central role in many applications in numerical analysis\, spatial statistics\, as well as statistical learning.  They appear in methods kno wn\, e.g.\, as radial basis functions\, kriging\, Gaussian processes\, or simply kernel-based methods.  Some kernels\, such as Gaussian kernel\, m ultiquadric kernel or the family of Matern kernels\, are very popular and are often used in a "one-size-fits-all" general purpose strategy.  In th is talk I will emphasize a different approach\; that of custom-built desi gner kernels that have certain desirable built-in properties such as\, e. g.\, periodicity\, satisfaction of boundary conditions\, or non-stationar ity.  After introducing a few different types of designer kernels I will illustrate their use with some examples from data fitting\, the numerical solution of PDEs\, and electrical power demand forecasting.  X-ALT-DESC;FMTTYPE=text/html:<html><head><title></title></head><body><p>Po sitive definite reproducing kernels (or covariance kernels) play a centra l role in many applications in numerical analysis\, spatial statistics\, as well as statistical learning.&nbsp\; They appear in methods known\, e. g.\, as radial basis functions\, kriging\, Gaussian processes\, or simply kernel-based methods.&nbsp\; Some kernels\, such as Gaussian kernel\, mu ltiquadric kernel or the family of Matern kernels\, are very popular and are often used in a "one-size-fits-all" general purpose strategy.&nbsp\; In this talk I will emphasize a different approach\; that of custom-built designer kernels that have certain desirable built-in properties such as \, e.g.\, periodicity\, satisfaction of boundary conditions\, or non-stat ionarity.&nbsp\; After introducing a few different types of designer kern els I will illustrate their use with some examples from data fitting\, th e numerical solution of PDEs\, and electrical power demand forecasting.&n bsp\;</p></body></html> DTSTART;TZID=America/Chicago:20190412T140000 DTEND;TZID=America/Chicago:20190412T150000 SEQUENCE:1 URL:https://math.missouristate.edu/ CATEGORIES:Current Students,Faculty,Future Students,Staff END:VEVENT END:VCALENDAR