Manual From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

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Topic 2 activities. Random variables.

Programming

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.

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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.

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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.

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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

Study design, data collection, results analysis and interpretation. There is a problem with the download and it throws an error. I belive that this book is not free This book is deprecrated This book is old and exists a newer version This book is absolutely useless Something different Give us your mail and will notify you when the problem is resolved. If you want to say something about, feel free to do it. Close Send feedback.

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OpenLibra uses cookies to ensure we give you the best experience. For more information about the cookies policy and uses click here. Enter your search keywords. Score: 0 votes. Sending vote. Since this is a computer science audience, a greater sophistication in programming can be assumed. Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modeling: What do probabilistic models really mean, in real-life terms? How does one choose a model? How do we assess the practical usefulness of models? For instance, the chapter on continuous random variables begins by explaining that such distributions do not actually exist in the real world, due to the discreteness of our measuring instruments.

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.