
Encyclopaedia of Biostatistics, Bioinformatics and Computer Applications
Description
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Biostatistics is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine and agriculture; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. Biostatistical reasoning and modelling were of critical importance to the foundation theories of modern biology. ln larger universities where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. ln general, the difference between a statistics program and a biostatistics one is twofold: (i) statistics departments will often host theoretical/methodological research which arc less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business and economics and biological areas other than medicine. Applications for machine learning include machine perception, computer vision, natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brain-machine interfaces and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing, software engineering, adaptive websites, robot locomotion, and structural health monitoring. This book provides deep insight to various dimensions of issues relating to development of this subject.
More details
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Content
- Cover
- Half Title
- Title
- Copyrights
- Contents
- Preface
- 1. Introduction
- 2. Collection of Data
- 3. Dispersion and Standard Deviation
- 4. Probability and Distributions
- 5. Analysis of Variance
- 6. Experimental Designs
- 7. Statistical Quality Control
- 8. Classification and Tabulation of Data
- 9. Measures of Central Tendency
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