This paper is concerned with trust modeling for networked computing systems. Of particular interest to this paper is the observation that trust is a subjective notion that is invisible, implicit and uncertain in nature, therefore it may be suitable for being expressed by subjective probabilities and then modeled on the basis of Bayesian principle. In spite of a few attempts to model trust in the Bayesian paradigm, the field lacks a global comprehensive overview of Bayesian methods and their theoretical connections to other alternatives. This paper presents a study to fill in this gap. It provides a comprehensive review and analysis of the literature, showing that a large deal of existing work, whether or not proposed based on Bayesian principle, can cast into a general Bayesian paradigm termed subjective Bayesian trust (SBT) theory here. The SBT framework can thus act as a general theoretical infrastructure for comparing or analyzing theoretical ties among existing trust models, and for developing novel models. The aim of this study is twofold. One is to gain insights about Bayesian philosophy in modeling trust. The other is to drive current research step ahead in seeking a high-level, abstract way of modeling and evaluating trust. A Survey on Trust Modeling from a Bayesian Perspective

egg, dataiku, Data Science Basics, deep learning, image classification

November 08, 2018 |

Claire Carroll

IntroductionElectronic video gaming has extended from being a hobby into a serious sport and business. Earlier this year, eSports officially became a medal event in the 2022 Asian Games. According to data analytics expert Andrew Pearson, the rise of eSports presents exciting opportunities in data analytics and marketing.

Conversational interfaces are transforming the way people interact with software applications and services. They are untethering people from keyboards and smartphone gestures by replacing those interfaces with a more natural style of interaction: the spoken word. Increasingly, people are opting to interact with a bot when they need an answer to a question, to set a reminder, or to obtain a product or service.

While there is more and more data available in structured formats (CSV, JSON) through initiatives like OpenData, sometimes nicely formatted data still not publicly available.

By Ashutosh Trivedi, Co-Founder at Spext

Yesterday I posted the following picture on Twitter and it quickly became my most visible tweet ever (by far):

Ruth Thomson, Interim Director of Strategic Innovation sat down with Jelena, one of Mango’s machine learning experts.

Before you start learning Python, choose the IDE that suits you the best. Gregory Piatetsky anticipates the next move in data science in his interview with Thomson Reuters. Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening other boosting libraries. Investigate the top 10 Python data science libraries. Check out highlights from the latest Burtch Works Study: Salaries of Predictive Analytics Professionals.

We’ve seen a ton of progress in machine learning during the past 12 months, with customers using Amazon SageMaker – a fully-managed service which has put ML into the hands of tens of thousands of developers and data scientists – to find fraud, predict pitches, and tune engines. We’ve added nearly 100 new features and capabilities since we introduced SageMaker at re:Invent last year, with the vast majority based on customer feedback (keep it coming). We continue that drum beat today, with major new announcements for Amazon SageMaker.