BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250613T235704Z UID:C83FB939-45CA-497A-AA90-117FCC6622FA DTSTART;TZID=America/New_York:20250605T190000 DTEND;TZID=America/New_York:20250605T200000 DESCRIPTION:We consider data-driven methods for modeling discrete-valued dy namical systems evolving over networks. The spread of viruses and diseases \, the propagation of ideas and misinformation\, the fluctuation of stock prices\, and correlations of financial risk between banking and economic i nstitutions are all examples of such systems. In many of these systems\, d ata may be widely available\, but approaches to identify relevant mathemat ical models\, including the underlying network topology\, are not widely e stablished or agreed upon. Classic system identification methods focus on identifying continuous-valued dynamical systems from data\, where the main analysis of such approaches largely focuses on asymptotic properties\, i. e.\, consistency. More recent identification approaches have focused on sa mple complexity\, i.e.\, how much data is needed to achieve an acceptable model approximation. In this talk\, we will discuss the problem of identif ying a mathematical model from data for a discrete-valued\, discrete-time dynamical system evolving over a network. Specifically\, under maximum lik elihood estimation approaches\, we will demonstrate guaranteed consistency conditions and sample complexity bounds. Applications to the aforemention ed examples will be further discussed as time allows.\n\nSpeaker(s): Dr. B eck\, \n\nVirtual: https://events.vtools.ieee.org/m/480080 LOCATION:Virtual: https://events.vtools.ieee.org/m/480080 ORGANIZER:joseph.palko@ieee.org SEQUENCE:40 SUMMARY:DISCRETE STATE SYSTEM IDENTIFICATION: EXAMPLES AND BOUNDS URL;VALUE=URI:https://events.vtools.ieee.org/m/480080 X-ALT-DESC:Description: <br /><p><span style="font-size: 10.5pt\; font-fami ly: 'Times New Roman'\,serif\; mso-fareast-font-family: 'Malgun Gothic'\; mso-fareast-theme-font: minor-fareast\; mso-ansi-language: EN-US\; mso-far east-language: EN-US\; mso-bidi-language: AR-SA\;">We consider data-driven methods for modeling discrete-valued dynamical systems evolving over netw orks. The spread of viruses and diseases\, the propagation of ideas and mi sinformation\, the fluctuation of stock prices\, and correlations of finan cial risk between banking and economic institutions are all examples of su ch systems. In many of these systems\, data may be widely available\, but approaches to identify relevant mathematical models\, including the underl ying network topology\, are not widely established or agreed upon. Classic system identification methods focus on identifying continuous-valued dyna mical systems from data\, where the main analysis of such approaches large ly focuses on asymptotic properties\, i.e.\, consistency. More recent iden tification approaches have focused on sample complexity\, i.e.\, how much data is needed to achieve an acceptable model approximation. In this talk\ , we will discuss the problem of identifying a mathematical model from dat a for a discrete-valued\, discrete-time dynamical system evolving over a n etwork. Specifically\, under maximum likelihood estimation approaches\, we will demonstrate guaranteed consistency conditions and sample complexity bounds. Applications to the aforementioned examples will be further discus sed as time allows.</span></p> END:VEVENT END:VCALENDAR