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Powering innovation in artificial intelligence with Open Access research...

Artificial intelligence (AI) and machine learning (ML) together represent a research frontier with a wide variety of discipline specific applications. At Strathclyde researchers in Computer & Information Sciences are exploring improved AI planning models to enable improved decision making by automous agents. Meanwhile, research conducted by the Aerospace Centre of Excellence is using AI and computational techniques to optimize the efficacy of certain astronautic applications. This includes 'global trajectory optimization' to aircraft and spacecraft design, from the planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes or satellites.

AI is also transforming the research agenda within Finance, where researchers are exploring the potential of AI and machine learning in evaluating banking risks and in new, emerging aspects of FinTech.

Explore some of this Open Access research from Computer & Information Sciences, Aerospace Centre of Excellence and Finance. Or explore all of Strathclyde's Open Access research...

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Number of items: 7.

2014

Fé, Eduardo (2014) Estimation and inference in regression discontinuity designs with asymmetric kernels. Journal of Applied Statistics, 41 (11). pp. 2406-2417. ISSN 0266-4763

2011

Holden, D. (2011) Testing for heteroskedasticity in the tobit and probit models. Journal of Applied Statistics, 38 (4). pp. 735-744. ISSN 0266-4763

Zhong, Mingjun and Girolami, Mark and Faulds, Karen and Graham, Duncan (2011) Bayesian methods to detect dye labelled DNA oligonucleotides in multiplexed raman spectra. Journal of Applied Statistics, 60 (2). pp. 187-206. ISSN 0266-4763

2006

Clarke, E.D. and Speirs, D.C. and Heath, M.R. and Wood, S.N. and Gurney, W.S.C. and Holmes, S.J. (2006) Calibrating remotely sensed chlorophyll-a data by using penalized regression splines. Journal of Applied Statistics, 55 (3). pp. 331-353. ISSN 0266-4763

Clarke, E.D. and Speirs, D.C. and Heath, M.R. and Wood, S.N. and Gurney, W.S.C. and Holmes, S.J. (2006) Corrigendum: Calibrating remotely sensed chlorophyll-a data by using penalized regression splines. Journal of Applied Statistics, 55 (4). pp. 551-552. ISSN 0266-4763

2004

Holden, D.R. (2004) Testing the normality assumption in the Tobit model. Journal of Applied Statistics, 31 (5). pp. 521-532. ISSN 0266-4763

1990

Robertson, C. (1990) A matrix regression model for the transition probabilities in a finite state stochastic-process. Journal of Applied Statistics, 39 (1). pp. 1-19. ISSN 0266-4763

This list was generated on Sun Aug 9 04:20:52 2020 BST.