Topic 2 activities. Random variables.
Define random variable, discrete random variable and continuous random variable. Define the probability function, the probability distribution function and the joint probability function. Link random variable indicators with sampling indicators. Explanation of topics: definition of random variable and of probability and probability distribution functions; random variable metrics and relationship to sample indicators. Objectives: 4 6 7 Contents: 2.
Block 2. Topic 3 activities. Random variable models Define the theoretical, discrete and continuous models typically used in the IT field and their characteristics and parameters. Tests to monitor pre-reading and study.
Explanation of topics: define the theoretical, discrete and continuous models typically used in the IT field and their characteristics and parameters and calculate direct and inverse probabilities with the defined models. Objectives: 5 6 Contents: 3. Block 3. Random variable models. Mid-semester exam 1 Mid-semester exam consisting of problems corresponding to topics 1 to 3 learning objectives 1 to 8. Objectives: 17 1 2 3 4 5 6 7 Week: 7 Outside class hours Type: theory exam. Topic 4 activities. Evidence: principles of inference Basic population, sampling, parameter and estimator concepts.
Introduction to statistics; definition and linking of confidence intervals CI and hypothesis testing HT.
APPLICATION OF STOCHASTIC NETWORKS CONVERSION TECHNIQUE FOR SIMULATION OF MOBILE BANKING ATTACKS
Explanation of topics: definition of sample, parameter, estimator and statistic for constructing confidence intervals CI and description of the statistics defining the more interesting CIs and HTs in an IT setting. Objectives: 8 9 10 Contents: 4. Block 4. Evidence: principles of inference. Topic 5 activities. Experiment design Define tests with independent and paired samples. Situate and specify the comparison of two means using Student-t, CIs and HTs in independent paired samples and the comparison of two variances in independent samples and suitable transformations.
Explanation of topics: comparison of means and variances. Problems: Model example of the topics Laboratory: Individual problem completion in E-status. Objectives: 11 12 Contents: 5. Block 5. Experiment design. Topic 6 activities. Statistical models and forecasting Define a relational model between two variables, analyse the variability, validate the premises, consider possible transformations and make predictions.
BLASTing small molecules—statistics and extreme statistics of chemical similarity scores
Explanation of topics: define the linear model, validate it and analyse transformations and make predictions. Objectives: 13 14 15 Contents: 6. Block 6. Statistical models and forecasting. Application activities Identify problems in the IT field for a probability or statistical study. Design a study, collect data and analyse and interpret results. Summarise conclusions critically.
Monitor studies and encourage synthetic and critical evaluations. Laboratory: Guidance and monitoring regarding practical probability and statistics components. Autonomous learning: Research computer situations where a probability or statistical study is necessary.
From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science
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Empirical Evaluation of a System Call-Based Android Malware Detector
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The continuous model is therefore just that--a model, and indeed a very useful model. There is considerable discussion of the intuition involving probabilistic concepts, and the concepts themselves are defined through intuition.