HumanBrain
1.0.0
1.0.0
  • What is notes
  • Knowledge Base
    • Machine Learning
      • Gausian Process
    • Math
      • Statistics
        • Importance Sampling
        • Probability And Counting
      • Linear Algebra
        • Dummy
    • Deep Learning
      • Deep Learning
  • Code
    • Code
      • Generative
      • NLP
      • RL
      • Vision
  • Papers
    • papers
  • Notes
    • Cognitive
      • (2016. 4) ML Learn And Think Like Human
    • Optimization
      • (2010. 5) Xavier Initialization
      • (2015. 2) Batch Normalization
      • (2015. 2) He Initialization
    • Reinforcement Learning
      • (2017. 6) Noisy Network Exploration
    • Vision
      • (2013. 12) Network In Network
      • (2014. 12) Fractional Max-pooling
      • (2015. 12) Residual Network
    • Natural Language Processing
      • (2014. 9) Bahdanau Attention
      • (2015. 11) Diversity Conversation
      • (2015. 11) Multi Task Seq2seq
      • (2015. 12) Byte To Span
      • (2015. 12) Vocabulary Strategy
      • (2015. 6) Skip Thought
      • (2015. 6) Teaching Machine Read And Comprehend
      • (2015. 8) Luong Attention
      • (2015. 8) Subword NMT
      • (2016. 10) Bytenet
      • (2016. 10) Diverse Beam Search
      • (2016. 10) Fully Conv NMT
      • (2016. 11) Bidaf
      • (2016. 11) Dual Learning NMT
      • (2016. 11) Generate Wiki
      • (2016. 11) NMT With Reconstruction
      • (2016. 2) Exploring Limits Of Lm
      • (2016. 3) Copynet
      • (2016. 4) NMT Hybrid Word And Char
      • (2016. 5) Adversarial For Semi Supervised Text Classification
      • (2016. 6) Sequence Knowledge Distillation
      • (2016. 6) Squad
      • (2016. 7) Actor Critic For Seq
      • (2016. 7) Attn Over Attn NN RC
      • (2016. 9) PS LSTM
      • (2017. 10) Multi Paragraph RC
      • (2017. 11) Neural Text Generation
      • (2017. 12) Contextualized Word For RC
      • (2017. 3) Self Attn Sentence Embed
      • (2017. 6) Slicenet
      • (2017. 6) Transformer
      • (2017. 7) Text Sum Survey
      • (2018. 1) Mask Gan
      • (2018. 2) Qanet
      • (2018. 5) Minimal Qa
    • Generative
      • (2013. 12) VAE
      • (2014. 6) Gan
      • (2016. 7) Seq Gan
    • Model
      • (2012. 7) Dropout
      • (2013. 6) Dropconnect
      • (2015. 7) Highway Networks
      • (2015. 9) Pointer Network
      • (2016. 10) Fast Weights Attn
      • (2016. 10) Professor Forcing
      • (2016. 3) Stochastic Depth
      • (2016. 7) Layer Normalization
      • (2016. 7) Recurrent Highway
      • (2017. 1) Very Large NN More Layer
      • (2017. 6) Relational Network
Powered by GitBook
On this page
  1. Knowledge Base
  2. Math
  3. Statistics

Importance Sampling

PreviousStatisticsNextProbability And Counting

Last updated 6 years ago

  • In statistics, importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution than the distribution of interest. It is related to umbrella sampling in computational physics. Depending on the application, the term may refer to the process of sampling from this alternative distribution, the process of inference, or both.

Simple Summary

  • Importance Sampling is a kinds of approximation technique.

  • Approximate the expected value by sampling rather than directly from the distribution p(x)

  • Each sample has an importance, and using it, finding the expectation.

  • g(x) = proposal distrubution and using Monte Carlo, compute E[H(X)]

images
images