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Announcements

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2018 announcement

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2017 announcement

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Course purpose

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Probability theory and mathematical statistics are an integral part of modern experimental physics. Calculation of errors, correct presentation of the result, risk assessment - all these are important components of the work of the physicist who decided to conduct an experiment and publish its results. At the same time, as practice shows, many scientists (and not just students) complain about the lack of practical skills in this area. This is due to the fact that the teaching of the theoretical aspect of probability theory is often well placed in technical universities, but the purely practical aspect is completely overlooked.

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In our course, we will try to analyze in detail the issues of the practical application of the methods of statistical physics in planning and processing the results of a physical experiment (using specific examples). Theoretical calculations will be mainly excluded from lectures and left for independent study.

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Course format

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The course is planned in the optional format once a week, while lectures will be held every second week, and practical classes (seminars) will be held between the lectures, discussing examples and solving problems from modern experimental physics and everyday life (including laboratory work) .

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Announcements of important events, as well as a discussion of any issues related to the course, are available in the Google group mipt-statmethods. We recommend you use your Google Account to sign up for a group, as it provides more opportunities to communicate and work with documents. All group members have access to the course materials at the address.

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Course structure (preliminary program)

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  1. \n

    Statistical decision-making theory.

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    1. Decisions in deterministic tasks.
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    2. Decisions in non-deterministic tasks, risk function.
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    3. Conditional probability, decision making strategies.
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    2. \n
  2. \n

    Basic concepts of probability theory.

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    1. Definitions of probability.
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    2. Function of plausibility.
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    3. Point and interval estimates of distribution parameters.
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    4. Confidence intervals.
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    3. \n
  3. \n

    Errors in physical experiment.

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    1. Statistical and systematic errors.
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    2. Properties of distributions at replacement of variables.
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    3. Uncorrector stacking.
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    4. Adding results of various experiments.
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    4. \n
  4. \n

    Properties of distributions.

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    1. Poisson's binomial distribution and distribution.
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    2. Normal distribution and its properties.
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    3. Average values, moments of distributions.
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    5. \n
  5. \n

    Checking statistical hypotheses.

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    1. Functions of random variables.
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    2. Statistical criteria and their properties.
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    3. Methods of criteria construction.
      4. \n
    4. Criteria of data agreement with the theory.
      5. \n
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    6. \n
  6. \n

    Evaluation of parameters.

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    1. Parameter criteria.
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    2. Maximum probability and chi-square method.
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    3. Using the probability function to construct the Chi-square maximum and Chi-square maximum. Interval estimates.
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    4. Interval estimates in the case of normal distribution.
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    7. \n
  7. \n

    Modern data analysis methods (optional).

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    1. Fitting of experimental curves. Criteria of phytate quality. Computer methods for solving optimization problems.
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    2. Multiparameter analysis. Analysis of correlations.
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    3. Fisher Information and its Application. Maximum information and its application.\nthe border between Rao and Kramer.
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    4. Two approaches to probability: frequency approach and subjective probability. The problem of unique events.
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    5. Using a computer to analyze experimental data.
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    8. \n
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Reporting

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The test takes place in the form of a presentation based on the materials of an individual project. Each student has the opportunity to prepare a report analyzing the results of a particular real or thought experiment (you can take laboratory work).

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Recommended literature

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