Past events

Wednesday Lunch: Privacy and Big Data

Wednesday, February 15, 2017 - 12:00

Speaker: Catharina Nes, Specialist director, research and analytics at the Norwegian Data Protection Authority (Datatilsynet).

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Wednesday Lunch: A gentle inroduction to Bayesian Nonparametrics

Wednesday, February 1, 2017 - 12:00

Speaker: Nils Lid Hjort, Professor at UiO, Dept of Mathematics

Abstract: Bayesian Nonparametrics is about prior distributions on big and complicated spaces, such as the set of all continuous densities or regression functions, and then working out the mathematics to characterise the posterior distributions, along with clever computational or simulation schemes for inferences, predictions, classifications. Such methods are finding their ways also into Machine Learning applications. I will provide a necessarily short and hopefully gentle introduction to the field.

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center

TUESDAY Statistics Seminar: Inge S. Helland

Tuesday, January 31, 2017 - 14:15

Speaker: Inge S. Helland (Professor emeritus at Department of Mathematics, UiO)

Title: Symmetry and model reduction

Abstract: Statistics is the basis for most empirical sciences, and an interesting question is whether one can find links to other, complementary scientific cultures by taking statistical theory as a point of departure. I will show that the answer is yes for at least two cases if one adds the following structure to the statistical model paradigm: By suitable symmetry assumptions there may be a group of transformations defined on the sample space and a corresponding group of transformations defined on the parameter space. If the parameter group is not transitive, it induces several orbits on the parameter space. I will postulate that any model reduction should be to an orbit or to a set of orbits of the chosen group. First I illustrate this rule by giving several statistical examples. Then I show how the rule leads to the partial least squares model, and I indicate how one can derive the quantum theory for electron spin in this way. Some recent results on quantum probability are mentioned.

Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

TUESDAY STATISTICS SEMINAR: Daniel Roy

Tuesday, December 20, 2016 - 14:15

Speaker: Daniel Roy (Department of Statistical Sciences, University of Toronto)

Title: Extended admissible if and only if nonstandard Bayes

Abstract:  For finite parameter spaces under finite loss, every Bayesian procedure derived from a prior with full support is admissible, and every admissible procedure is Bayes. This relationship begins to break down as we move to continuous parameter spaces. Under some regularity conditions, admissible procedures can be shown to be the limits of Bayesian procedures. Under additional regularity, they are generalized Bayesian, i.e., they minimize the Bayes risk with respect to an improper prior. In both these cases, one must venture beyond the strict confines of Bayesian analysis. Using methods from mathematical logic and nonstandard analysis, we introduce the class of nonstandard Bayesian decision procedures---namely, those whose Bayes risk with respect to some prior is within an infinitesimal of the optimal Bayes risk.  Without any regularity conditions, we show that a decision procedure is extended admissible if and only if its nonstandard extension is nonstandard Bayes. We apply the nonstandard theory to derive a purely standard theorem: on a compact parameter space, every extended admissible estimator is Bayes if the risk function is continuous.
Joint work with Haosui Duanmu.​

This will be the last TUESDAY STATISTICS SEMINAR of 2016. We look forward to see you all again in 2017!

Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

Wednesday Lunch: Forecasting power systems

Wednesday, November 30, 2016 - 12:00

Speaker: Alex Lenkoski, Senior Research Scientist at the Norwegian Computing Center

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center

TUESDAY STATISTICS SEMINAR: Peter Müller

Tuesday, November 22, 2016 - 14:15

Speaker: Peter Müller (University of Texas at Austin)

Title: Dynamic treatment regimes - Nonparametric Bayes for causal inference

Abstract: We discuss inference for multi-stage clinical trials. The motivating example are multi-stage chemotherapy regimes for acute leukemia. Patients were randomized among initial chemotherapy treatments but not among later salvage therapies. We propose a Bayesian nonparametric (BNP) approach to account for the lack of randomization in the later stages. We argue that the BNP approach can provide an objective evaluation of a causal effect of competing treatment regimens, adjusting for the lack of randomization. In a simulation study we compare the BNP approach with standard doubly robust causal inference methods and show how the BNP approach compares favorably as an objective method that does not rely on particular model assumptions for a response or model for treatment assignment. The paper is Xu Y, Mueller P, Wahed A and Thall P., "Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times." JASA, in press https://arxiv.org/abs/1405.2656

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Wednesday Lunch: Sensor systems

Wednesday, November 16, 2016 - 12:00

Speaker: Ingrid Glad, Professor at UiO, Dept of Mathematics

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

TUESDAY STATISTICS SEMINAR: Per Mykland

Tuesday, November 1, 2016 - 14:15

Speaker: Per Mykland (University of Chicago)

Title: The Algebra of Two Scales Estimation - High Frequency Estimation that is Robust to Sampling Times

Abstract: In this paper, we show a new algebraic property of two scales estimation in high frequency data, under which the effect of sampling times is cancelled to high order. This is a particular robustness property of the two scales estimator. In general, irregular times can cause problems in estimators based on equidistant observation (trading or quote) times.

The new algebraic property can be combined with pre-averaging, and also presents a solution for handling asynchronously observed multivariate data. In connection with this development, we use the algebraic approach to define a version of two scales estimation which has no edge effect in
microstructure noise. Finally, the paper develops a central limit theory.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Wednesday Lunch: Personalised fraud detection

Wednesday, October 19, 2016 - 12:00

Speaker: Anders Løland, Assistant Research Director, Norwegian Computing Center

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Mini-workshop on Clicking data

Friday, October 14, 2016 - 13:00

Welcome to the third BigInsight mini-workshop on a theme which is important across many of our projects.
The workshop is reserved to members of BigInsight.
Date: Friday 14 October
Time: 13:00-15:00
No registration is required; please just join.

Location: 
Norsk Regnesentral, room Alfa-Omega

TUESDAY STATISTICS SEMINAR: Tamara Broderick

Tuesday, October 11, 2016 - 14:15

Speaker: Tamara Brocerick (Massachusetts Institute of Technology)

Title: Fast Quantification of Uncertainty and Robustness with Variational Bayes

Abstract: In Bayesian analysis,the posterior follows from the data and a choice of a prior and a likelihood. These choices may be somewhat subjective and reasonably vary over some range. Thus, we wish to measure the sensitivity of posterior estimates to variation in these choices. While the field of robust Bayes has been formed to address this problem, its tools are not commonly used in practice---at least in part due to the difficulty of calculating robustness measures from MCMC draws. We demonstrate that, by contrast to MCMC, variational Bayes (VB) techniques are readily amenable to robustness analysis. Since VB casts posterior inference as an optimization problem, its methodology is built on the ability to calculate derivatives of posterior quantities with respect to model parameters. We use this insight to develop local prior robustness measures for mean-field variational Bayes (MFVB), a particularly popular form of VB due to its fast runtime on large data sets. A potential problem with MFVB is that it has a well-known major failing: it can severely underestimate uncertainty and provides no information about covariance. We generalize linear response methods from statistical physics to deliver accurate uncertainty estimates for MFVB---both for individual variables and coherently across variables. We call our method linear response variational Bayes (LRVB).

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

Mini-workshop on Anomalies detection

Monday, October 10, 2016 - 13:00

Welcome to the second BigInsight mini-workshop on a theme which is important across many of our projects.
The workshop is reserved to members of BigInsight.
Date: Monday 10 October
Time: 13:00-15:00
No registration is required; please just join.

Location: 
Matematisk Institutt, 12th floor, room 1259 Abels utsikt

Mini-workshop on Networks

Friday, September 30, 2016 - 10:00

Welcome to the first BigInsight mini-workshop on a theme which is important across many of our projects.
The workshop is reserved to members of BigInsight.
Date: Friday 30 September
Time: 10:00-12:00
No registration is required; please just join.

Location: 
Norsk Regnesentral, room Alfa-Omega

Wednesday Lunch: Personalised marketing

Wednesday, September 28, 2016 - 12:00

Speaker: Kjersti Aas, Assistant Research Director, Norwegian Computing Center

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Spiseriet, Norwegian Computing Center