Dear Friends and Colleagues,
On Tuesday the 20th of December we will have the Workshop “Stochastic Sauna Seminar”. The event will take place from 9am to 4pm at the University of Helsinki in Porthania building (Room P674) Yliopistonkatu 3. Afterwards we will go to Sauna at Löyly and have dinner there. For more details see the website
Dear fellow scientists,
If you are keen in exploring how to analyse data in a scientifically objective way, then please join the seminar on The Key Concepts Of Modern Statistical Thinking with Application. The seminar takes place in Economicum, sh.3 – 4, Arkadiankatu 7, Helsinki on the 23rd of August from 12.30 – 15.30h. You can find description of the seminar here . The speakers are:
Dr. Ana Kolar – a Visiting Researcher at Department of Mathematics and Statistics, University of Helsinki, and a founder of Tarastats, Helsinki, Finland. .
Dr. Per Johansson – a Professor at Department of Statistics, Uppsala University, Sweden, and at the Yau Mathematical Sciences Center, Tsinghua University, Beijing, China.
Analysing data in a scientifically objective way is crucial for an evidence-based society, so save the date and join the conversation.
seminar by Professor Ian Dryden (University of Nottingham) on June 3rd (monday) at 11.15 in seminar room D123 (Exactum, Kumpula campus, University of Helsinki). Abstract.
Course website , contents , contact: Prof. Sangita Kulathinal
Latent tree models are graphical models defined on a tree, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. This lecture offers a concise introduction to the theory of latent tree models. I will emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned.
This lecture course is divided into three parts. In part 1, I will present basic combinatorial concepts related to trees and tree metrics. In part 2, I will define latent tree graphical models and discuss their basic properties. I will also discuss linear latent tree models which provide a convenient general family of distributions whose second-order moment structure is tree-like. In the last part I will present main ideas used in the design of learning procedures for this model class. This includes the structural EM algorithm and various distance based methods.
This course will be based on my book
P. Zwiernik, “Semialgebraic statistics and latent tree models”, Chapman&Hall, 2015,
and a forthcoming chapter in “Handbook of Graphical Models”, see also arXiv:1708.00847.
Backward stochastic differential equations (BSDEs for short) have been introduced since the 90s, and have proved since then to be a fundamental tool in stochastic analysis, stochastic control, and even PDE analysis, with numerous applications in finance, economics and insurance. This course would be the occasion to provide an introduction to the theory as well as its latest developments. After going through some of the most important theoretical results, we will see as an illuminating application how BSDEs allow to treat in a general fashion several problems stemming from contract theory with moral hazard.
FDNSS 