Nicola Bilstein is glad to announce that the article "From third party to significant other for service encounters: a systematic review on third-party roles and their implications" co-authored with Liliane Abboud (The University of Manchester, UK), Nabila As'ad (University of Porto, Portugal), Annelies Costers (KU Leuven, Belgium) and Bieke Henkens and Katrien Verleye (both Ghent University, Belgium) is now forthcoming in the Journal of Service Management (VHB-JQ3: B).
The corresponding article is available online first as:
Abboud, L., As’ad, N., Bilstein, N., Costers, A., Henkens, B., & Verleye, K. From third party to significant other for service encounters: a systematic review on third-party roles and their implications, in: Journal of Service Management, DOI 10.1108/JOSM-04-2020-0099.
Folgender Gastvortrag am Do., 14.1.2021, 14 Uhr, ist (auch) Teil der iTIME-Vortragsreihe:
Titel des Vortrags: Bayesian Decision Making
There are often high expectations on the business impact that data science can produce. Unfortunately, various barriers often hinder the full realization of that impact, starting with communication challenges between different parts of the organization with different backgrounds. To bridge this chasm, data scientists and stake-holders should agree on a business relevant loss function to optimize. This way, any improvements in the model can directly be measured in their impact on the bottom line.
In this talk, I will show how probabilistic programming frameworks like PyMC3 can be used to solve an applied problems with an example from capital allocation. This approach allows us to accurately and flexibly map a real-world problem to a statistical model that can be quickly iterated and improved on. I will then show how the results of such a model, which are usually arcane and non-actionable posterior probability distributions, can be coupled with a loss function based on business mechanics, to (i) derive business related outcome measures, and (ii) suggest the optimal decision to make, rather than inform it.
Thomas Wiecki is the Chief Executive Officer at PyMC Labs. Prior to that Thomas was the lead data science researcher at Quantopian, where he used probabilistic programming and machine learning to help build the world's first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences across the US, Europe, and Asia. He holds a PhD in Computational Cognitive Neuroscience from Brown University.