Archives - School of Mathematics and Statistics

Probability & Statistics Seminar

TracieGrisim — Mon, 20 Mar 2023 13:18:26 +0000

Title: Individual model selection
Speaker: Prince Osei, �Ӱ�ԭ�� University
Location: HP 4351 (MacPhail Room) – �Ӱ�ԭ�� University
Date: Friday, March 24, 2023
Time: 1:00 – 2:00 pm

��ٰ��:�� In the study of decision-making patterns by individuals, such as the Iowa gambling task (IGT) process, there are several competing models suitable for describing a participant or group of individuals. Selecting a single best overall model typically comes at the cost of misallocating some individuals for the sake of the population. The calibration of the models requires re-running these models several times whenever an individual or set of individuals are moved or reshuffled among these models. Usually, there is a fixed computational budget, and can only afford to run each model once. An individualized model selection strategy that uses the Bayesian information criterion (BIC) is proposed. The method is computationally efficient and easy to implement to allocate the subjects to these competing models. The proposed individualized model selection strategy is illustrated using a hand-picked sample from the IGT data set.

Applied Analysis Day – �Ӱ�ԭ�� University

TracieGrisim — Tue, 18 Oct 2022 15:23:58 +0000

Date: Friday, November 4, 2022
Time: 8:45 a.m. – 5:10 p.m.
Room: Herzberg building, room 4351 (MacPhail Room)
Organizers: Emmanuel Lorin & Abbas Momeni
_____________________________________________________

The goal of this event is to bring together all nearby researchers (professors, graduate students, PDF) in this area, in order to exchange recent results, and to inspire new ideas and collaborations. This year the event will be host by �Ӱ�ԭ�� University, and we will be in particular interested in the recent developments in Scientific Machine Learning, which is become a very hot topic in applied mathematics.

Applied Probability Seminar

TracieGrisim — Wed, 20 Jul 2022 11:39:29 +0000

Date: July 22, 2022
Time: 11:00 a.m.
Location: Faculty of Science – 3230 Herzberg building (Dean’s boardroom), �Ӱ�ԭ�� University
On-line through zoom:
Cost: Free
Speaker: Dr. Jing Gai ( Royal Military College of Canada)
Title: The exact analyses of GI/M^a,b/C Queueing System

��ٰ��:�� Bulk-service queueing systems have been widely applied in many areas in daily life, for example, a group (or batch) of customers in transportation systems need to be served simultaneously. While the single-server queueing systems work in some of the cases, the multi-servers are unavoidable to efficiently handle most of the complex applications. Compared to the well-developed single-service or single-server queueing systems, the multi-server queueing systems are more complex and harder to deal with, especially when the interarrival-time distribution is arbitrary.

In this talk, the exact analyses to determine queue-length distributions for a complex bulk-service, multi-server queueing system GI/M^a,b/C where interarrival times follow an arbitrary distribution are derived. The introduction of quorum “a” further increases the complexity of the model, a two-dimensional Markov chain has to be involved. An elegant analytic solution and an efficient algorithm to obtain the queue-length distributions at three different epochs, i.e., pre-arrival epoch (p.a.e.), random epoch (r.e.) and post-departure epoch (p.d.e.) are presented, when the servers are in busy and idle states, respectively. The closed-form relations for these probabilities at three epochs are derived by using a standard level crossing analysis. The waiting-time distribution is discussed. The Little’s Formula for the system GI/M^a,b/C is verified theoretically and numerically. The closed form formulas for calculating the moments of queue-length at three epochs and the mean waiting-time will be provided.

Probability and Statistics Seminar

traciebarkley — Tue, 29 Mar 2022 13:24:41 +0000

Date & Time: April 1, 2022, at 2:00 pm
Location: Virtual, on Zoom see below for details
Cost: Free
Speaker: Maadooliat, Mehdi (Marquette University)
Title: Functional Singular Spectrum Analysis

Abstract: : In this talk, we introduce a new extension of the Singular Spectrum Analysis (SSA) called functional SSA to analyze functional time series. The new methodology is developed by integrating ideas from functional data analysis and univariate SSA. We explore the advantages of the functional SSA in terms of simulation results and two real data applications. We compare the proposed approach with Multivariate SSA (MSSA) and dynamic Functional Principal Component Analysis (dFPCA). The results suggest that further improvement to MSSA is possible, and the new method provides an attractive alternative to the dFPCA approach that is used for analyzing correlated functions. We implement the proposed technique to an application of remote sensing data and a call center dataset. We have also developed an efficient and user-friendly R package and a shiny web application to allow interactive exploration of the results.

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Probability and Statistics Seminar

traciebarkley — Mon, 24 Jan 2022 15:24:29 +0000

Date & Time: January 28, 2022 at 2:00 p.m.
Location: University of Ottawa (virtual)
Cost: Free
Speaker: Mahmoud Torabi (University of Manitoba)
Title: Analysing COVID-19 Data in the Canadian Province of Manitoba: A New Approach

��ٰ��:�� SEIR (susceptible-exposed-infected-removed) model is an appropriate model for analyzing infectious diseases such as influenza and COVID-19. However, in the SEIR model, it is assumed that the population of study is homogeneous and in this model one can’t incorporate other information (e.g., location of infected people, distance between susceptible and infected individuals, risk factors) which are important in predicting e.g. COVID-19 cases.

Recently, a geographically dependent individual-level model (GD-ILM) with SEIR framework was developed when spatio-temporal information and individual-level data are available. In the GD-ILM, the corresponding parameters of SEIR model (such as contact rate, incubation period, infectious period) are used for the entire population assuming the population study is homogeneous which is a basic assumption in the SEIR model. However, as we know, it is a strong assumption to assume the entire population study is homogeneous as different health regions of population study may act differently. In this talk, we propose to use a GD-ILM for each health region of Manitoba (central Canadian province) population and show it is more accurate than assuming the same SEIR model for entire population. In particular, Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters, we accurately predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas.

Performance of the proposed approach is also evaluated through simulation studies.

For more information: Please contact Mahmoud Zarepour, University of Ottawa zarepour@uOttawa.ca

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Herzberg Lecture – Increasing Diversity in Computer Science at All Levels

traciebarkley — Tue, 17 Nov 2020 16:21:32 +0000

Herzberg Lecture
Date: November 18, 2020
Time: 7:00 p.m.
Place: via Zoom webinar

“Increasing Diversity in Computer Science at All Levels”

Maria Klawe, President
Harvey Mudd College

For details and to register, click here: