21,001 research outputs found
Analysis on the evolution process of BFW-like model with explosive percolation of multiple giant components
Recently, the modified BFW model on random graph [Phys. Rev. Lett., 106,
115701 (2011)], which shows a strongly discontinuous percolation transition
with multiple giant components, has attracted much attention from physicists,
statisticians and materials scientists. In this paper, by establishing the
theoretical expression of evolution equations on the modified BFW model, the
steady-state and evolution process are analyzed and a close correspondence is
built between the values of parameter \alpha and the number of giant components
in steady-states, which fits very well with the numerical simulations. In fact,
with the value of \alpha decreasing to 0.25, the error between theoretical and
numerical results is smaller than 4% and trends to 0 rapidly. Furthermore, the
sizes of giant components for different evolution strategies can also be
obtained by solving some constraints derived from the evolution equations. The
analysis of the steady-state and evolution process is of great help to explain
why the percolation of modified BFW model is explosive and how explosive it is.Comment: 12 pages, 5 figure
Objective Bayes and Conditional Frequentist Inference
Objective Bayesian methods have garnered considerable interest and support among statisticians,
particularly over the past two decades. It has often been ignored, however, that in
some cases the appropriate frequentist inference to match is a conditional one. We present
various methods for extending the probability matching prior (PMP) methods to conditional
settings. A method based on saddlepoint approximations is found to be the most
tractable and we demonstrate its use in the most common exact ancillary statistic models.
As part of this analysis, we give a proof of an exactness property of a particular PMP in
location-scale models. We use the proposed matching methods to investigate the relationships
between conditional and unconditional PMPs. A key component of our analysis is a
numerical study of the performance of probability matching priors from both a conditional
and unconditional perspective in exact ancillary models. In concluding remarks we propose
many routes for future research
Biologists meet statisticians: A workshop for young scientists to foster interdisciplinary team work
Life science and statistics have necessarily become essential partners. The
need to plan complex, structured experiments, involving elaborated designs, and
the need to analyse datasets in the era of systems biology and high throughput
technologies has to build upon professional statistical expertise. On the other
hand, conducting such analyses and also developing improved or new methods,
also for novel kinds of data, has to build upon solid biological understanding
and practise. However, the meeting of scientists of both fields is often
hampered by a variety of communicative hurdles - which are based on
field-specific working languages and cultural differences.
As a step towards a better mutual understanding, we developed a workshop
concept bringing together young experimental biologists and statisticians, to
work as pairs and learn to value each others competences and practise
interdisciplinary communication in a casual atmosphere. The first
implementation of our concept was a cooperation of the German Region of the
International Biometrical Society and the Leibnitz Institute DSMZ-German
Collection of Microorganisms and Cell Cultures (short: DSMZ), Braunschweig,
Germany. We collected feedback in form of three questionnaires, oral comments,
and gathered experiences for the improvement of this concept. The long-term
challenge for both disciplines is the establishment of systematic schedules and
strategic partnerships which use the proposed workshop concept to foster mutual
understanding, to seed the necessary interdisciplinary cooperation network, and
to start training the indispensable communication skills at the earliest
possible phase of education
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