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The syllabus covers stochastic processes, Markov chains and jump processes, survival models, lifetime distribution estimation (including Kaplan–Meier and Cox models), multi-state Markov models, maximum likelihood estimation, graduation processes, and life table testing. Learning outcomes emphasize applying Markov and survival concepts and deriving maximum likelihood estimators.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/st338-15-actuarial-models-module-description/399000/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/st338-15-actuarial-models-module-description/399000.png","ImageObject",300,407,{"name":92,"@type":93},"OmBimo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-27",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What topics are included in the ST338-15 actuarial modelling syllabus?","Question",{"text":113,"@type":114},"The module covers actuarial modelling principles, stochastic processes, Markov chains and Markov jump processes, survival models, lifetime estimation (Kaplan–Meier and Cox models), multi-state Markov models, graduation, and statistical tests for life tables.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What are the expected learning outcomes by the end of the module?",{"text":118,"@type":114},"Students should be able to describe actuarial modelling and stochastic-process principles, define and apply Markov chains and Markov jump processes, explain survival models, and derive maximum likelihood estimators for transition intensities in multi-state settings.",{"name":120,"@type":111,"acceptedAnswer":121},"How is student study time and assessment structured for this module?",{"text":122,"@type":114},"The module uses a mix of lectures, tutorials, and private study, totaling 150 hours (including required lecture and tutorial sessions). Assessment is indicated as multiple, with private study focused on weekly revision, wider actuarial reading, practice exercises, and preparation for class tests and the examination.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},399000,1790528067,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962090893581,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","ST338-15 Actuarial Models  \n26/27  \nDepartment  \nStatistics  \nLevel  \nUndergraduate Level 3  \nModule leader  \nConor Hughes  \nCredit value  \n15  \nModule duration  \n10 weeks  \nAssessment  \nMultiple  \nStudy location  \nUniversity of Warwick main campus, Coventry  \nDescription  \nIntroductory description  \nThis module introduces the modelling techniques employed by actuaries. This module brings together a range of statistical techniques including stochastic processes, Markov chains, Survival models, estimation, maximum likelihood estimators and life tables to cover actuarial areas such as risk modelling, survival analysis and actuarial mathematics. It thus offers the opportunity to see how statistics is employed in an actuarial and thus authentic setting.  \nThis module is available for students on a course where it is a listed option and as an Unusual Option to students who have the required background as specified in the pre-requisite modules.  \nPre-requisites:  \n• Statistics Students:  \n○ ST227 Stochastic Processes, and ST228 Mathematical Methods for Statistics and Probability, and ST229 Probability for Mathematical Statistics, and ST230 Mathematical Statistics.  \n• Non-Statistics Students:  \n○ ST121 Statistical Laboratory, AND  \n○ ST227 Stochastic Processes, AND  \n○ ST232/ST233 Introduction to Mathematical Statistics or ST352 Introduction to  \nMathematical Statistics (for Finalists) .  \nUseful background. Students taking this module may benefit from taking ST346 Generalised Linear Models for Regression and Classification since it may help students comprehend or learn the material more effectively but it is not required to study this module.  \nResults from this module may be partly used to determine exemption eligibility in the Institute and Faculty of Actuaries (IFoA) modules CS2 and CM1 . (Independent application with the IFoA maybe required to receive the exemption. )  \nModule web page  \nModule aims  \nTo cover part of the syllabus for Institute of Actuaries exam CS2 .  \nOutline syllabus  \nThis is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.  \nThis module covers the following actuarial areas:  \n• Principles of actuarial modelling  \n• Principles of stochastic processes  \n• Markov chains and Markov jump processes  \n• Survival models: lifetimes, curtate future lifetime, expected value and variance.  \n• Estimation procedures for lifetime distributions: Kaplan—Meier estimate, Cox model  \n• Multi-state Markov models.  \n• Maximum likelihood estimators for transition intensities in multi-state models.  \n• Estimation in the Markov Model.  \n• Estimating mortality rates by age: exact methods, census approximations  \n• Process of graduation.  \n• Statistical tests for life tables.  \nLearning outcomes  \nBy the end of the module, students should be able to:  \n• Describe the principles of actuarial modelling  \n• Describe the general principles of stochastic processes and their classification into different types.  \n• Define and apply a Markov chain.  \n• Define and apply a Markov jump process.  \n• Explain the concept of survival models.  \n• Describe estimation procedures for lifetime distributions  \n• Derive maximum likelihood estimators for the transition intensities in models of transfers  \nbetween states with piecewise constant transition intensities.  \n• Describe the two-state model of a single decrement and compare its assumptions with those of the random lifetime model, derive maximum likelihood estimators for transition intensities and state the Poisson approximation to the estimator in the case of a single decrement.  \n• Describe how to estimate transition intensities depending on age, exactly or using the census approximation.  \n• Describe how to test crude estimates for consistency with a standard table or set of graduated estimates and describe the process of graduation.  \n• Explain heterogeneity in populations, and simple methods to deal with heter","cbCaisf0l6bYAdNG","https://ap.wps.com/l/cbCaisf0l6bYAdNG","pdf",23398,"English","# Module aims\n## Outline syllabus\n## Learning outcomes\n## Indicative reading list","[{\"question\":\"What topics are included in the ST338-15 actuarial modelling syllabus?\",\"answer\":\"The module covers actuarial modelling principles, stochastic processes, Markov chains and Markov jump processes, survival models, lifetime estimation (Kaplan–Meier and Cox models), multi-state Markov models, graduation, and statistical tests for life tables.\"},{\"question\":\"What are the expected learning outcomes by the end of the module?\",\"answer\":\"Students should be able to describe actuarial modelling and stochastic-process principles, define and apply Markov chains and Markov jump processes, explain survival models, and derive maximum likelihood estimators for transition intensities in multi-state settings.\"},{\"question\":\"How is student study time and assessment structured for this module?\",\"answer\":\"The module uses a mix of lectures, tutorials, and private study, totaling 150 hours (including required lecture and tutorial sessions). Assessment is indicated as multiple, with private study focused on weekly revision, wider actuarial reading, practice exercises, and preparation for class tests and the examination.\"}]","ST338-15 Actuarial Models - Module description | PDF",1790491007,15]