diff --git a/doc/pub/Bayesian/html/._Bayesian-bs000.html b/doc/pub/Bayesian/html/._Bayesian-bs000.html index 426734bac..73bb9265d 100644 --- a/doc/pub/Bayesian/html/._Bayesian-bs000.html +++ b/doc/pub/Bayesian/html/._Bayesian-bs000.html @@ -41,7 +41,7 @@ Automatically generated HTML file from DocOnce source
  • What is Bayesian Statistics
    • +
  • Why Bayesian Statistics?
  • Inference
  • Statistical Inference
  • Some history
    • @@ -175,7 +175,7 @@ MathJax.Hub.Config({
    [3] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Jun 7, 2019

    +

    Jun 8, 2019


    diff --git a/doc/pub/Bayesian/html/._Bayesian-bs001.html b/doc/pub/Bayesian/html/._Bayesian-bs001.html index 94eea8f61..9c7d303a7 100644 --- a/doc/pub/Bayesian/html/._Bayesian-bs001.html +++ b/doc/pub/Bayesian/html/._Bayesian-bs001.html @@ -41,7 +41,7 @@ Automatically generated HTML file from DocOnce source

  • What is Bayesian Statistics
    • +
  • Why Bayesian Statistics?
  • Inference
  • Statistical Inference
  • Some history
    • @@ -151,19 +151,29 @@ MathJax.Hub.Config({ -

    What is Bayesian Statistics

    +

    Why Bayesian Statistics?

    -Morten's original plan: Reminder about probabilities from the statistics section -

      -
    1. Product rule
      2. -
    2. Binomial distribution
      3. -
    3. Gaussian PDF
      4. -
    4. other PDFs
      5. -
    5. Bayesian regression analysis
      6. -
    +

    +We have already made ourselves familiar with elements of a statistical +data analysis via quantities like the bias-variance tradeoff as well +as some central distribution functions such as the Normal +distribution, the binomial distribution and other probability +distribution functions. + +

    +In essentially all the Machine Learning +algorithms we have studied, our focus has been on a so-called +frequentist approach, where knowledge of an underlying likelihood +function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. + +

    +Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. +We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem. + +

    diff --git a/doc/pub/Bayesian/html/Bayesian-bs.html b/doc/pub/Bayesian/html/Bayesian-bs.html index 426734bac..73bb9265d 100644 --- a/doc/pub/Bayesian/html/Bayesian-bs.html +++ b/doc/pub/Bayesian/html/Bayesian-bs.html @@ -41,7 +41,7 @@ Automatically generated HTML file from DocOnce source
  • What is Bayesian Statistics
    • +
  • Why Bayesian Statistics?
  • Inference
  • Statistical Inference
  • Some history
    • @@ -175,7 +175,7 @@ MathJax.Hub.Config({
    [3] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Jun 7, 2019

    +

    Jun 8, 2019


    diff --git a/doc/pub/Bayesian/html/Bayesian-reveal.html b/doc/pub/Bayesian/html/Bayesian-reveal.html index 1573a0d48..74170c6a2 100644 --- a/doc/pub/Bayesian/html/Bayesian-reveal.html +++ b/doc/pub/Bayesian/html/Bayesian-reveal.html @@ -153,7 +153,7 @@ MathJax.Hub.Config({

    [3] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Jun 7, 2019

    +

    Jun 8, 2019


    @@ -164,19 +164,27 @@ MathJax.Hub.Config({

    -

    What is Bayesian Statistics

    +

    Why Bayesian Statistics?

    -Morten's original plan: Reminder about probabilities from the statistics section +We have already made ourselves familiar with elements of a statistical +data analysis via quantities like the bias-variance tradeoff as well +as some central distribution functions such as the Normal +distribution, the binomial distribution and other probability +distribution functions. + +

    +In essentially all the Machine Learning +algorithms we have studied, our focus has been on a so-called +frequentist approach, where knowledge of an underlying likelihood +function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. + +

    +Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. +We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem. + -

      -

    1. Product rule
      2. -

    2. Binomial distribution
      3. -

    3. Gaussian PDF
      4. -

    4. other PDFs
      5. -

    5. Bayesian regression analysis
      6. -
    diff --git a/doc/pub/Bayesian/html/Bayesian-solarized.html b/doc/pub/Bayesian/html/Bayesian-solarized.html index 836aea2f3..596cea2b5 100644 --- a/doc/pub/Bayesian/html/Bayesian-solarized.html +++ b/doc/pub/Bayesian/html/Bayesian-solarized.html @@ -61,7 +61,7 @@ div { text-align: justify; text-justify: inter-word; } +

    Jun 8, 2019












    -

    What is Bayesian Statistics

    +

    Why Bayesian Statistics?

    -Morten's original plan: Reminder about probabilities from the statistics section -

      -
    1. Product rule
      2. -
    2. Binomial distribution
      3. -
    3. Gaussian PDF
      4. -
    4. other PDFs
      5. -
    5. Bayesian regression analysis
      6. -
    +

    +We have already made ourselves familiar with elements of a statistical +data analysis via quantities like the bias-variance tradeoff as well +as some central distribution functions such as the Normal +distribution, the binomial distribution and other probability +distribution functions. + +

    +In essentially all the Machine Learning +algorithms we have studied, our focus has been on a so-called +frequentist approach, where knowledge of an underlying likelihood +function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. + +

    +Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. +We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem. + +

    diff --git a/doc/pub/Bayesian/html/Bayesian.html b/doc/pub/Bayesian/html/Bayesian.html index 533aa79b6..4bceafc90 100644 --- a/doc/pub/Bayesian/html/Bayesian.html +++ b/doc/pub/Bayesian/html/Bayesian.html @@ -66,7 +66,7 @@ div { text-align: justify; text-justify: inter-word; } +

    Jun 8, 2019












    -

    What is Bayesian Statistics

    +

    Why Bayesian Statistics?

    -Morten's original plan: Reminder about probabilities from the statistics section -

      -
    1. Product rule
      2. -
    2. Binomial distribution
      3. -
    3. Gaussian PDF
      4. -
    4. other PDFs
      5. -
    5. Bayesian regression analysis
      6. -
    +

    +We have already made ourselves familiar with elements of a statistical +data analysis via quantities like the bias-variance tradeoff as well +as some central distribution functions such as the Normal +distribution, the binomial distribution and other probability +distribution functions. + +

    +In essentially all the Machine Learning +algorithms we have studied, our focus has been on a so-called +frequentist approach, where knowledge of an underlying likelihood +function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. + +

    +Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. +We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem. + +

    diff --git a/doc/pub/Bayesian/ipynb/Bayesian.ipynb b/doc/pub/Bayesian/ipynb/Bayesian.ipynb index 7b7a82bc4..09b2fe4b8 100644 --- a/doc/pub/Bayesian/ipynb/Bayesian.ipynb +++ b/doc/pub/Bayesian/ipynb/Bayesian.ipynb @@ -12,24 +12,28 @@ "\n", " **Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Jun 7, 2019**\n", + "Date: **Jun 8, 2019**\n", "\n", "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", "\n", - "## What is Bayesian Statistics\n", - "Morten's original plan: Reminder about probabilities from the statistics section\n", - "1. Product rule\n", + "## Why Bayesian Statistics?\n", "\n", - "2. Binomial distribution\n", + "We have already made ourselves familiar with elements of a statistical\n", + "data analysis via quantities like the bias-variance tradeoff as well\n", + "as some central distribution functions such as the Normal\n", + "distribution, the binomial distribution and other probability\n", + "distribution functions. \n", "\n", - "3. Gaussian PDF\n", + "In essentially all the Machine Learning\n", + "algorithms we have studied, our focus has been on a so-called\n", + "**frequentist approach**, where knowledge of an underlying likelihood\n", + "function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. \n", "\n", - "4. other PDFs\n", - "\n", - "5. Bayesian regression analysis\n", + "Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. \n", + "We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem.\n", "\n", "\n", "\n", diff --git a/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz b/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz index 661504cb9..d4a65fc47 100644 Binary files a/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz and b/doc/pub/Bayesian/ipynb/ipynb-Bayesian-src.tar.gz differ diff --git a/doc/pub/Bayesian/pdf/Bayesian-minted.pdf b/doc/pub/Bayesian/pdf/Bayesian-minted.pdf index 426bb53d5..4a27f562a 100644 Binary files a/doc/pub/Bayesian/pdf/Bayesian-minted.pdf and b/doc/pub/Bayesian/pdf/Bayesian-minted.pdf differ diff --git a/doc/src/Bayesian/Bayesian.do.txt b/doc/src/Bayesian/Bayesian.do.txt index 760e23e56..90f65f225 100644 --- a/doc/src/Bayesian/Bayesian.do.txt +++ b/doc/src/Bayesian/Bayesian.do.txt @@ -5,14 +5,23 @@ DATE: today !split -===== What is Bayesian Statistics ===== +===== Why Bayesian Statistics? ===== !bblock -Morten's original plan: Reminder about probabilities from the statistics section -o Product rule -o Binomial distribution -o Gaussian PDF -o other PDFs -o Bayesian regression analysis + +We have already made ourselves familiar with elements of a statistical +data analysis via quantities like the bias-variance tradeoff as well +as some central distribution functions such as the Normal +distribution, the binomial distribution and other probability +distribution functions. + +In essentially all the Machine Learning +algorithms we have studied, our focus has been on a so-called +_frequentist approach_, where knowledge of an underlying likelihood +function has not been emphasized. Our data, whether we had a classification or a regression problem, have been our central points of departure. + +Here we wish to merge this approach with the derivation of a likelihood function which can be used to make prediction on how our system under study evolves. +We will venture into the realm of what is called Bayesian Neural Networks. To get an overarching view on what this entails, the following figure conveys the essential differences between a standard Neural network that we have met earlier and a Bayesian Neural Network. In order to get there, we need to present some of the basic elements of Bayesian statistics, starting with the product rule and Bayes' theorem. + !eblock !split diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt index c4f14d9d1..dc3f15f14 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -18,7 +18,7 @@ chapters = { 'Linalg': 'Review of central linear algebra elements', 'Statistics': 'Monte Carlo methods and elements of probability theory', 'Regression': 'Regression Methods', - 'Splines': 'Gradient methods', + 'Splines': 'Gradient methods and Minimization Algorithms', 'LogReg': 'Logistic Regression', 'NeuralNet': 'Neural Networks', 'DimRed': 'Reduction of dimensionality', @@ -29,7 +29,7 @@ chapters = { 'Autoencoders': 'Autoencoders', 'Reinforce': 'Reinforcement Learning', 'odenn': 'Solving ordinary and partial differential equations with Neural Networks', - 'Bayesian': 'Elements of Bayesian theory', + 'Bayesian': 'Elements of Bayesian theory and Bayesian Neural Networks', 'summary': 'Summary', } %> diff --git a/doc/web/course.html b/doc/web/course.html index 675973cce..50cfe61ab 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -6,6 +6,7 @@ Automatically generated HTML file from DocOnce source + Overview of course material: Data Analysis and Machine Learning @@ -83,7 +84,10 @@ div { text-align: justify; text-justify: inter-word; } None, '___sec4'), ('Regression Methods', 2, None, '___sec5'), - ('Gradient methods', 2, None, '___sec6'), + ('Gradient methods and Minimization Algorithms', + 2, + None, + '___sec6'), ('Logistic Regression', 2, None, '___sec7'), ('Neural Networks', 2, None, '___sec8'), ('Reduction of dimensionality', 2, None, '___sec9'), @@ -104,7 +108,10 @@ div { text-align: justify; text-justify: inter-word; } 2, None, '___sec16'), - ('Elements of Bayesian theory', 2, None, '___sec17'), + ('Elements of Bayesian theory and Bayesian Neural Networks', + 2, + None, + '___sec17'), ('Summary', 2, None, '___sec18'), ('Python and Scikit Learn, a short guide', 2, None, '___sec19'), ('Teach yourself C++', 2, None, '___sec20'), @@ -366,7 +373,7 @@ formulas in HTML or ipython notebook files. -

    Gradient methods

    +

    Gradient methods and Minimization Algorithms

    -

    Elements of Bayesian theory

    +

    Elements of Bayesian theory and Bayesian Neural Networks