MATH486 — Probability and Statistical Inference III
Covers advanced probability and statistics including multivariate distributions, maximum likelihood estimation, Bayesian inference, and advanced methods.
Learning Objectives & Matches
Calculate the probabilities and expected values associated to functions of multiple variables
Analyze multivariate probability distributions and their properties
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
Report results of statistical analyses, including information in the form of graphs, charts, and tables.
Analyze, manipulate, or process large sets of data using statistical software.
Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
Develop software applications or programming for statistical modeling and graphic analysis.
Clean and manipulate raw data using statistical software.
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.
Perform computations and apply methods of numerical analysis to data.
Analyze clinical data using appropriate statistical tools.
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
Apply techniques of moment generating functions
Apply maximum likelihood estimation for parameter inference
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.
Develop software applications or programming for statistical modeling and graphic analysis.
Develop or apply data mining and machine learning algorithms.
Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.
Analyze, manipulate, or process large sets of data using statistical software.
Draw conclusions or make predictions, based on data summaries or statistical analyses.
Use the maximum likelihood method to find estimators
Perform Bayesian statistical inference using prior and posterior distributions
Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data.
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
Develop or use mathematical models to track changes in biological phenomena, such as the spread of infectious diseases.
Specify inputs accessed by the system and plan the distribution and use of the results.
Clean and manipulate raw data using statistical software.
Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques.
Specify inputs accessed by the system and plan the distribution and use of the results.
Conduct logical analyses of business, scientific, engineering, and other technical problems, formulating mathematical models of problems for solution by computers.
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
Construct probability tables for events such as fires, natural disasters, and unemployment, based on analysis of statistical data and other pertinent information.
Investigate properties of a statistical estimator based on characteristics of bias, efficiency, consistency and sufficiency
Use advanced statistical methods including EM algorithm and bootstrap
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
Adapt statistical methods to solve specific problems in many fields, such as economics, biology, and engineering.
Analyze clinical data using appropriate statistical tools.
Report results of statistical analyses, including information in the form of graphs, charts, and tables.
Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
Report results of statistical analyses in peer-reviewed papers and technical manuals.
Clean and manipulate raw data using statistical software.
Perform computations and apply methods of numerical analysis to data.
Develop or apply data mining and machine learning algorithms.
Apply the Rao-Blackwell theorem to identify minimum variance unbiased estimators
Apply statistical decision theory to optimization problems
Develop and analyze statistical models for complex data
Use computational methods for statistical inference
Develop or implement data analysis algorithms.
Determine project plans, timelines, or technical objectives for statistical aspects of biological research studies.
Develop software applications or programming for statistical modeling and graphic analysis.
Analyze, manipulate, or process large sets of data using statistical software.
Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.
Write program code to analyze data with statistical analysis software.
Develop mathematical or statistical models of phenomena to be used for analysis or for computational simulation.
Draw conclusions or make predictions, based on data summaries or statistical analyses.
Develop and test experimental designs, sampling techniques, and analytical methods.
Select methods, techniques, or criteria for data warehousing evaluative procedures.