Getting started in probabilistic graphical models

Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses that are testable at the bench? This note sketches out some answers and illustrates the main ideas behind the statistical approach to biological pattern discovery.Comment: 12 pages, 1 figur

Integrating modes of policy analysis and strategic management practice : requisite elements and dilemmas

There is a need to bring methods to bear on public problems that are inclusive, analytic, and quick. This paper describes the efforts of three pairs of academics working from three different though complementary theoretical foundations and intervention backgrounds (i.e., ways of working) who set out together to meet this challenge. Each of the three pairs had conducted dozens of interventions that had been regarded as successful or very successful by the client groups in dealing with complex policy and strategic problems. One approach focused on leadership issues and stakeholders, another on negotiating competitive strategic intent with attention to stakeholder responses, and the third on analysis of feedback ramifications in developing policies. This paper describes the 10 year longitudinal research project designed to address the above challenge. The important outcomes are reported: the requisite elements of a general integrated approach and the enduring puzzles and tensions that arose from seeking to design a wide-ranging multi-method approach

Improving The Understanding Of The Arithmetic Concept Through Realistic Mathematic Education (RME)

This study aims to improve the arithmetic concept understanding of the second grade students of Percobaan 2 Yogyakarta elementary school through Realistic Mathematic Education (RME). This study was a classroom action research. This study was conducted in two cycles. The participants were the students and teacher of second grade (IIA) of Percobaan 2 Yogyakarta Elementary School. The data were collected by observation form, manual interview, and test. Observations were used to collect the data of the students’s activities in mathematics teaching and learning. The test was used to collect the data of the students’s arithmetic concept understanding. The results of the study show that RME improve the students’s arithmetic concept understanding of Percobaan 2 Yogyakarta Elementary School. There are the improvements of arithmetic concept understanding after doing mathematic teaching and learning through RME. The improvements are: (a) repeating a concept ability increases from 79.9798% to 84.3434%, (b) classifying objects according their characteristics increases from 70,9090% to 86.3636% , (c) giving the example and non-example ability increase from 94.8864% to 95.4545%, (d) presenting the concept in all of represented mathematics increases from 73.8634% to 89.3939%, (e) developing the sufficient and necessary condition ability of a concept increases from 46.9697% to 79.5454%, (f) Using and deciding a particular procedure increases from 71.9697% to 78.0303%, (g) aplying concept increases from 71.2121% to 78,7879%. Generally, the arithmetic concept understanding increases from 72.7273% to 85.0000%. Key words: arithmetic concept understanding, realistic mathematic educatio

BFO and DOLCE: So Far, So Close…

A survey of the similarities and differences between BFO and DOLCE, and of the mutual interactions between Nicola Guarino and Barry Smit

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Memories of Professor Witold Klonecki

The article presents memories of Witold Klonecki

Economic evaluation using decision analytical modelling : design, conduct, analysis, and reporting

Economic evaluations are increasingly conducted alongside randomised controlled trials, providing researchers with individual patient data to estimate cost effectiveness. However, randomised trials do not always provide a sufficient basis for economic evaluations used to inform regulatory and reimbursement decisions. For example, a single trial might not compare all the available options, provide evidence on all relevant inputs, or be conducted over a long enough time to capture differences in economic outcomes (or even measure those outcomes). In addition, reliance on a single trial may mean ignoring evidence from other trials, meta-analyses, and observational studies. Under these circumstances, decision analytical modelling provides an alternative framework for economic evaluation. Decision analytical modelling compares the expected costs and consequences of decision options by synthesising information from multiple sources and applying mathematical techniques, usually with computer software. The aim is to provide decision makers with the best available evidence to reach a decision—for example, should a new drug be adopted? Following on from our article on trial based economic evaluations, we outline issues relating to the design, conduct, analysis, and reporting of economic evaluations using decision analytical modelling