Thu, August 30, 2018
Public Access

Category: All

August 2018
Mon Tue Wed Thu Fri Sat Sun
    1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31    
2:00pm [2:00pm] Kriti Goel
Thursday, 30 August 2018 Room 215, 2.00-3.30 Speaker: Kriti Goel Title: Mixed multiplicities of ideals Abstract: The concept of Hilbert-Samuel polynomial for an m-primary ideal was extended for two m-primary ideals by P. B. Bhattacharya. In other words, the function l(R/I^rJ^s) is given by a polynomial for r, s large. The coefficients appearing in the highest total degree terms in the polynomial are called the mixed multiplicities. These were investigated by B. Teissier (and J. J. Risler) in his Cargese paper. In a series of two talks, we will look at some properties of mixed multiplicities, using superficial elements. These talks aim to cover the preliminaries required for reading the paper 'A generalization of an inequality of Lech relating multiplicity and colength' by C. Huneke, I. Smirnov and J. Validashti.

[3:00pm] Dr. Iker Perez, Assistant Professor in Statistics at the University of Nottingham, UK
Speaker: Dr. Iker Perez, Assistant Professor in Statistics at the University of Nottingham, UK Date: 30th Aug 2018 Time 3-4 pm Venue: Ramanujan Hall Title: Approximate Uncertainty Quantification with Jump Processes Abstract: This talk will discuss foundational statistical challenges and probabilistic considerations associated with families of stochastic jump models, which often find applications in domains such as genetics, epidemiology, mathematical biology or operations research. I will review Markov jump processes, and by means of common accessible examples, discuss the strong impediments posed by real-world application scenarios to inverse uncertainty quantification tasks. Next, I will discuss current statistical advances linked to structured jump systems along with relevant literature. Through a model exemplar borrowed from queueing theory, I will finally present an approximate and scalable variational Bayesian framework, suitable for uncertainty quantification tasks with a large class of structured processes. The talk will further include examples with applications of the methods introduced.