{"id":1210,"date":"2016-06-30T11:36:53","date_gmt":"2016-06-30T11:36:53","guid":{"rendered":"https:\/\/www.science.nus.edu.sg\/?p=1210"},"modified":"2019-11-06T11:38:09","modified_gmt":"2019-11-06T11:38:09","slug":"adaptive-simulation-methods","status":"publish","type":"post","link":"https:\/\/www.science.nus.edu.sg\/blog\/2016\/06\/adaptive-simulation-methods\/","title":{"rendered":"Adaptive simulation methods"},"content":{"rendered":"<div id=\"t3-content\" class=\"t3-content col-xs-12\">\n<div class=\"item-page\" itemscope=\"\" itemtype=\"https:\/\/schema.org\/Article\">\n<article itemscope=\"\" itemtype=\"http:\/\/schema.org\/Article\">\n<section class=\"article-content clearfix\" itemprop=\"articleBody\">30\u00a0Jun 2016. NUS scientists demonstrate that adaptive sequential Monte Carlo algorithms used in finance or genetics can provide good predictions without user input.In many applications, we require user input which is often difficult to obtain. Prof Ajay JASRA and the research team from the Department of Statistics and Applied Probability in NUS have shown that they can simulate this input and obtain good results even without user input [1].<\/p>\n<p>These algorithms are called \u201csequential Monte Carlo\u201d (SMC) algorithms and have been used since the mid-90s. They can be highly effective in producing important statistical estimates, but often the algorithms require input information and expert knowledge to work well. They have been used for many applications, as described below. In these applications, the algorithms were used in situations where the input of the algorithms is adaptive, i.e. that input information is learnt by the algorithm. The issue is to show that these algorithms still return the correct integral; an unanswered question until [1].<\/p>\n<p>The \u201cadaptive\u201d SMC algorithms have been used in many contexts; such as in finance [2] and genetics [3]. At the Risk Management Institute at NUS, it is used in the Credit research initiative <a href=\"http:\/\/www.rmicri.org\/home\/\">http:\/\/www.rmicri.org\/home\/.<\/a> This is a non-profit project using adaptive SMC for assistance in predicting credit risk rates. The website offers daily predictions from a probability of default model for about 60,400 listed firms in the Asia Pacific, North America, Europe, Latin America, the Middle East and Africa. The impact of these predictions reaches the economies (potentially any private and public institution or individual) of these zones.<\/p>\n<p>The research in [1] is potentially a first work of many for adaptive SMC. The mathematical assumptions used to show the validity could be further refined to allow even more universality of the results. In addition, the ideas could be used to further develop new and even more powerful algorithms for large datasets.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" alt=\"10. JasraA STA 20160601 1\" src=\"https:\/\/www.science.nus.edu.sg\/wp-content\/uploads\/2019\/11\/10.-JasraA_STA_20160601_1.jpg\" width=\"488\" height=\"188\" \/><\/p>\n<p>Figure shows the use of adaptive SMC for field estimation (used in weather prediction). The left is the true field and the right the estimated field by adaptive SMC. The algorithm which is used can be found in [4].<\/p>\n<p>&nbsp;<\/p>\n<p><strong>References<\/strong><\/p>\n<p>[1] Beskos, A., Jasra, A., Kantas, N. &amp; Thiery A. \u201cOn the convergence of adaptive sequential Monte Carlo methods\u201d. Annals of Applied Probability. 26 (2016) 1111-1146.<\/p>\n<p>[2] Jasra, A., Stephens, D., Doucet A. &amp; Tsagaris, T. \u201cInference for L\u00e9vy driven stochastic volatility<\/p>\n<p>models via adaptive sequential Monte Carlo\u201d. Scandinavian Journal of Statistics. 38 (2011) 1-22.<\/p>\n<p>[3] Del Moral, P., Doucet, A. &amp; Jasra A. \u201cAn adaptive sequential Monte Carlo method for approximate Bayesian Computation\u201d. Statistics and Computing. 22 (2012) 1009-1020.<\/p>\n<p>[4] Kantas, N., Beskos, A. &amp; Jasra A. \u201cSMC for inverse problems: A case study for the Navier Stokes Equation\u201d. SIAM\/ASA Journal on Uncertainty Quantification. 2 (2014) 464-489.<\/p>\n<\/section>\n<\/article>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>30\u00a0Jun 2016. NUS scientists demonstrate that adaptive sequential Monte Carlo algorithms used in finance or genetics can provide good predictions&#8230;<\/p>\n","protected":false},"author":1,"featured_media":1211,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[17,13],"tags":[],"class_list":["post-1210","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science","category-research-news"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.6 (Yoast SEO v23.6) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Adaptive simulation methods - NUS Faculty of Science<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.science.nus.edu.sg\/blog\/2016\/06\/adaptive-simulation-methods\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adaptive simulation methods\" \/>\n<meta property=\"og:description\" content=\"30\u00a0Jun 2016. 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