BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Stats Camp Statistics Course - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.statscamp.org
X-WR-CALDESC:Events for Stats Camp Statistics Course
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20230312T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20231105T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20241001T130000
DTEND;TZID=America/Chicago:20241004T170000
DTSTAMP:20260411T054843
CREATED:20220701T034911Z
LAST-MODIFIED:20241003T155435Z
UID:2570-1727787600-1728061200@www.statscamp.org
SUMMARY:Bayesian Statistics Course
DESCRIPTION:LIVESTREAM and/or ASYNCHRONOUS – 4-day Bayesian Data Analysis Statistics Course\nDOWNLOAD SAMPLE COURSE SLIDES AND WATCH COURSE VIDEO PREVIEW\nCourse Overview:\nUnlock the Power of Bayesian Statistics: Join Our Intensive Five-Day Bayesian Course \nAre you ready to take your statistical skills to the next level? Look no further than our comprehensive five-day Bayesian statistics course. As one of our most popular training workshops each year\, this course offers advanced students\, faculty\, and researchers from all disciplines a unique opportunity to dive deep into Bayesian data analysis with expert instructors Mauricio Garnier-Villarreal\, Ph.D. and Esteban Montenegro-Montenegro\, Ph.D. \nPropel Your Career Forward:\n– Academic Research: Publish groundbreaking studies and contribute to cutting-edge research in your field.\n– Market Research: Gain insights into consumer behavior\, market trends\, and product performance.\n– Healthcare: Analyze patient outcomes\, healthcare delivery systems\, and treatment effectiveness.\n– Public Policy: Address societal challenges through evidence-based recommendations.\n– Environmental Science: Develop strategies for sustainability and conservation efforts. \nSkills Gained:\n– Advanced understanding of Bayesian statistics principles\n– Proficiency in Bayesian data analysis techniques\n– Ability to apply Bayesian methods to complex research problems\n– Skills in model estimation\, validation\, and interpretation\n– Expertise in handling and interpreting Bayesian output\n– Competence in using statistical software for Bayesian analysis\n– Familiarity with advanced topics such as hierarchical modeling and Markov chain Monte Carlo (MCMC) methods \nSeminar Topics:\n\nThe rich information provided by Bayesian analysis and how it differs from traditional (Frequentist) statistical analysis\nThe concepts of Bayesian reasoning along with the easy math and intuitions for Bayes’ rule\nThe concepts and hands-on use of modern algorithms (“Markov chain Monte Carlo”) that achieve Bayesian analysis for realistic applications\nHow to use the free software R for Bayesian analysis.\nAn extensive array of applications\, including comparison of two groups\, ANOVA-like designs\, linear regression\, logistic regression\, multilevel regression\, and growth models\, count regression\, robust regression\, regularization and polynomials\, and missing data treatments in Bayesian inference.\n\nBayesian Statistics Seminar Description:\nMany fields of science are transitioning from null hypothesis significance testing (NHST) to Bayesian Data Analysis Solutions. Bayesian analysis provides rich information about the relative credibilities of all candidate parameter values for any descriptive model of the data\, without reference to p values. Bayesian analysis applies flexibly and seamlessly to complex hierarchical models and realistic data structures\, including small samples\, large samples\, unbalanced designs\, missing data\, censored data\, outliers\, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic models. This seminar shows you how to do Bayesian data analysis\, hands on. \n\nInstructors: Mauricio Garnier Villarreal\, Ph.D.\n \n\n\nResearcher from Costa Rica\, working at the Sociology department from the Vrije Universiteit Amsterdam (VU). Overall my work is about development and test of data analysis methods\, and their applications in a variety of applied fields. \nI am passionate about open source\, and collaborative research. \n\nEsteban Montenegro Montenegro\, Ph.D.\n \n\n\nI’m an Assistant Professor at the Psychology and Child Development Department. I focus my research ideas in problems related to quantitative psychology. I’m always doing research on latent variable modeling. I applied my passion for statistics in topics related to healthy aging and dementia. \n  \nBayesian Statistics Course Includes:\nMaterials\, downloads\, recorded course video viewable for up to one year. \n\n\n\nLearning Objectives\nLearning Objectives:\nAfter engaging in course lectures and discussions as well as completing the hands-on practice activities with real data\, participants will be able to:\n\nDescribe the fundamental properties of Bayesian reasoning.\nImplement Bayes’ rule\nUtilize Markov Chain Monte Carlo in the brms package for R to evaluate Bayesian models.\nApply the appropriate prior distribution to a Bayesian analysis.\nConduct generalized linear models using Bayesian estimation\, including simple linear regression\, multiple regression\, and robust regression.\nConduct a T-test using Bayesian estimation.\nConduct a Bayesian ANOVA.\nEvaluate Logistic and Count regression models using Bayesian estimation.\nEvaluate Multilevel linear regression models using Bayesian estimation.\nConduct a Bayesian Multilevel growth curve analysis.\nUnderstand how issues of missing data are addressed in the Bayesian framework.\nExplain the difference between frequentist and Bayesian statistics\nCritically evaluate applications of Bayesian analysis in scientific studies.\nAnalyze data using Bayesian techniques in R.\nSpecify\, estimate\, evaluate\, and compare different Bayesian models to fit possible hypothesis.\n\nParticipants are encouraged to bring data of interest to work with during the week\, there will be time to work on it with help of the instructor. Work on datasets of interest will magnify the learning and impact of the course. \nParticipants will also complete the course with a foundation for future learning about Bayesian statistics and knowledge about available resources to guide such endeavors. \nSeminar Prerequisites\nBayesian Data Analysis Solutions Prerequisites:\nRequired: \n\nBasic proficiency in multiple linear regression\, the generalized linear model.\nIntermediate proficiency with at least one statistical software package (e.g.\, SPSS\, Stata\, SAS\, R\, etc.).\n\nNot required but advantageous: \n\nAt least limited experience (e.g.\, graduate-level course) with multilevel models\nIntermediate proficiency in R\, or syntax base software\n\nNo level of proficiency beyond basic awareness is assumed for skills related to: \n\nBayesian Statistics\nData analysis using R\n\nSoftware and Computer Support\nBayesian Statistics Software and Computer Support:\nIt is important to bring a notebook computer to the seminar\, so you can run the programs and see how their output corresponds with the presentation material.  Please install the software before arriving at the seminar. We’ll be estimating the examples in R language using the packages rstan and brms. The latter will be the main package in this course. You can read more about brms package: https://mc-stan.org/users/interfaces/brms. \nSeminar Audience\nBayesian Statistics Course Audience:\nThe intended audience is advanced students\, faculty\, and other researchers\, from all disciplines\, who want a ground-floor introduction to doing Bayesian data analysis. \nSeminar Files\nBayesian Statistics Course Files\nBelow are links to seminar files for those who enrolled in the seminar. Please download these files onto your computer before the first day of the seminar. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it. \nInstructor will provide password on first day of seminar:\nClick Here to Access Bayesian Data Analysis Seminar Files \nAll statistical software used at Stats Camp will be available\, free to participants\, on our SMORS (statistical modeling on remote servers) system for the duration of camp.Syllabus\n\n\n\nStats Camp 2024: Bayesian Data Analysis\n\n\nTuesday\nOctober 1\, 2024\n\n\n9:00–10:45\nBrief introduction to R and Rstudio\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nKnow your distributions. Application of priors\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nGeneral linear model. Multiple regression\, and robust regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nGeneral linear model. Robust regression\n\n\nWednesday\nOctober 2\, 2024\n\n\n9:00–10:45\nModel comparison\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nGeneral linear model. T-test and ANOVA\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nRegularization and polynomials in linear regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nGeneralized linear Model. Logistic regression\n\n\nThursday\nOctober 3\, 2024\n\n\n9:00–10:45\nGeneralized linear Model: Count regression\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMultilevel linear regression\n\n\n12:30–1:30\nRest Break\n\n\n1:30–3:15\nMultilevel linear regression\n\n\n3:15–3:30\nRest Break\n\n\n3:30–5:00\nMultilevel growth curve/repeated measures\n\n\nFriday\nOctober 4\, 2024\n\n\n9:00–10:45\nIntroduction to missing data\n\n\n10:45–11:00\nRest Break\n\n\n11:00–12:30\nMissing data in Bayesian inference\n\n\n12:30–1:30\nRest Break\n\n\n1:30~3:30\nIndividual Consultations\n\n\n\nDownload Sample Slides and Preview Course Video\nPlease fill out and submit the form below to get instant access to sample course materials.
URL:https://www.statscamp.org/courses/bayesian-statistics-course/
LOCATION:Livestream and/or Asynchronous:\, If you are unavailable to join live via Zoom\, you can participate asynchronously by viewing the recorded course videos for up to 1 year.
CATEGORIES:Bayesian Data Analysis Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.statscamp.org/wp-content/uploads/2022/06/bayesian-data-analysis-seminar-1.jpg
END:VEVENT
END:VCALENDAR