Go Back to 617-141 (Mathematical Sciences, Faculty of Science, v4, p204)
NOTE: These differences were detected by computer program - they may or may not be substantive.
Different CONTENT
Source=[Introduction to programming: algorithms, simple data types, assignment, conditionals, iteration, functions and procedures, complex data types, array processing. Numerical methods: number representation, errors, numerical integration, solution of linear and nonlinear equations. Probability: basic probability theory, conditional probability and independence, law of total probability and Bayes' theorem. Elementary distribution theory: cumulative distribution function and quantiles; probability mass function and probability density function. Discrete and continuous distributions - using binomial and normal distributions as examples. Uniform number generators. Simulation of observations on a given distribution. Simulation of probability models. Application of the computer to simulation.]
Xref = [Introduction to programming Algorithms, simple data types, assignment, conditionals, iteration, functions and procedures, complex data types, array processing. Numerical methods Number representation, errors, numerical integration, solution of linear and nonlinear equations. Probability Basic probability theory. Conditional probability and independence. Law of total probability and Bayes' theorem. Elementary distribution theory: cumulative distribution function and quantiles; probability mass function and probability density function. Discrete and continuous distributions - using binomial and normal distributions as examples. Uniform number generators. Simulation of observations on a given distribution. Simulation of probability models. Application of the computer to simulation.]
Different OBJECTIVES
Source=[On completion of this subject, students should:
<p><i>Comprehend:</i></p>
<ul>
<li>the syntax of a programming language;
<li>the terminology of probability and the principles of probability modelling.
</ul>
<p><i>Have developed:</i></p>
<ul>
<li>the ability to read, write and adapt computer programs;
<li>skills in reformulation of problems in a form suitable for computer solution;
<li>the ability to use established numerical methods;
<li>the ability to carry out probability calculations using standard distributions;
<li>the ability to make an appropriate choice of model for standard situations;
<li>the ability to write programs to simulate simple probability models.
</ul>
<p><i>Appreciate:</i></p>
<ul>
<li>the structure of a programming language, its potential and limitations;
<li>the application of probability modelling in describing the real world;
<li>the concept of randomness.
</ul>]
Xref = [On completion of this subject, students should:
<p>Comprehend:</p>
<ul>
<li>the syntax of a programming language;
<li>the terminology of probability and the principles of probability modelling.
</ul>
<p>Have developed:</p>
<ul>
<li>the ability to read, write and adapt computer programs;
<li>skills in reformulation of problems in a form suitable for computer solution;
<li>the ability to use established numerical methods;
<li>the ability to carry out probability calculations using standard distributions;
<li>the ability to make an appropriate choice of model for standard situations;
<li>the ability to write programs to simulate simple probability models.
</ul>
<p>Appreciate:</p>
<ul>
<li>the structure of a programming language, its potential and limitations;
<li>the application of probability modelling in describing the real world;
<li>the concept of randomness.
</ul>]
Differences in Math. & Stats., Faculty of Educ(Parkville) (v5, p150)
Different CONTACT
Source=[39 lectures (three a week), 24 hours practical (two hours a week), 12 x 1-hour tutorials and 12 hours project work.]
Xref = [39 lectures (three each week), 24 hours practical (two hours each week), 12 1-hour tutorials and 12 hours project work.]
Different CONTENT
Source=[Introduction to programming: algorithms, simple data types, assignment, conditionals, iteration, functions and procedures, complex data types, array processing. Numerical methods: number representation, errors, numerical integration, solution of linear and nonlinear equations. Probability: basic probability theory, conditional probability and independence, law of total probability and Bayes' theorem. Elementary distribution theory: cumulative distribution function and quantiles; probability mass function and probability density function. Discrete and continuous distributions - using binomial and normal distributions as examples. Uniform number generators. Simulation of observations on a given distribution. Simulation of probability models. Application of the computer to simulation.]
Xref = [<i>Introduction to programming </i>Algorithms, simple data types, assignment, conditionals, iteration, functions and procedures, complex data types, array processing. <i>Numerical methods</i> Number representation, errors, numerical integration, solution of linear and nonlinear equations. <i>Probability </i>Basic probability theory. Conditional probability and independence. Law of total probability and Bayes' theorem. Elementary distribution theory: cumulative distribution function and quantiles; probability mass function and probability density function. Discrete and continuous distributions - using binomial and normal distributions as examples. Uniform number generators. Simulation of observations on a given distribution. Simulation of probability models. Application of the computer to simulation.]
Different SEMESTER
Source=[Semester 1]
Xref = [First semester.]
Mon Oct 9 16:30:34 1995
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