SlideShare a Scribd company logo

A Bayesian Approach to Data Compression

•Download as PPTX, PDF•
•
Joe Suzuki
Joe Suzuki

This document outlines a two-part course on Bayesian approaches to data compression. Part I on July 17th will cover data compression for known and unknown sources over 90 minutes, including a 45-minute exercise. Part II on July 24th will focus on learning graphical models from data based on the concepts from Part I.

Science
1 of 23
A Bayesian Approach to Data Compression Joe Suzuki July 17, 2017
Road Map PART-I: July 17, 2017 1. Data Compression for Known Source (30 mins) 2. Data Compression for Unknown Source (15 mins) 3. Exercise (45 mins) PART-II: July 24, 2017 Learning a graphical model from data (based on PART-I)
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression
A Bayesian Approach to Data Compression

Recommended

Forest Learning from Data
Forest Learning from DataForest Learning from Data
Forest Learning from DataJoe Suzuki
 
ePOM - Intro to Ocean Data Science - Scientific Computing
ePOM - Intro to Ocean Data Science - Scientific ComputingePOM - Intro to Ocean Data Science - Scientific Computing
ePOM - Intro to Ocean Data Science - Scientific ComputingGiuseppe Masetti
 
SAP webinar: Explaining Keras Image Classification Models with LIME
SAP webinar: Explaining Keras Image Classification Models with LIMESAP webinar: Explaining Keras Image Classification Models with LIME
SAP webinar: Explaining Keras Image Classification Models with LIMEShirin Elsinghorst
 
What We Learned Becoming 3rd Party Recruiters
What We Learned Becoming 3rd Party RecruitersWhat We Learned Becoming 3rd Party Recruiters
What We Learned Becoming 3rd Party RecruitersVested Technology
 
AWE USA 2018 Startup Pitch: Alper Guler with Kabaq
AWE USA 2018 Startup Pitch: Alper Guler with KabaqAWE USA 2018 Startup Pitch: Alper Guler with Kabaq
AWE USA 2018 Startup Pitch: Alper Guler with KabaqAugmentedWorldExpo
 
Ruhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Introducing Deep Learning with Keras and PythonRuhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Introducing Deep Learning with Keras and PythonShirin Elsinghorst
 
ePOM - Intro to Ocean Data Science - Data Visualization
ePOM - Intro to Ocean Data Science - Data VisualizationePOM - Intro to Ocean Data Science - Data Visualization
ePOM - Intro to Ocean Data Science - Data VisualizationGiuseppe Masetti
 
20081101 1000sp
20081101 1000sp20081101 1000sp
20081101 1000spK Yamaguchi
 

Recommended

Forest Learning from Data
Forest Learning from DataForest Learning from Data
Forest Learning from DataJoe Suzuki
 
ePOM - Intro to Ocean Data Science - Scientific Computing
ePOM - Intro to Ocean Data Science - Scientific ComputingePOM - Intro to Ocean Data Science - Scientific Computing
ePOM - Intro to Ocean Data Science - Scientific ComputingGiuseppe Masetti
 
SAP webinar: Explaining Keras Image Classification Models with LIME
SAP webinar: Explaining Keras Image Classification Models with LIMESAP webinar: Explaining Keras Image Classification Models with LIME
SAP webinar: Explaining Keras Image Classification Models with LIMEShirin Elsinghorst
 
What We Learned Becoming 3rd Party Recruiters
What We Learned Becoming 3rd Party RecruitersWhat We Learned Becoming 3rd Party Recruiters
What We Learned Becoming 3rd Party RecruitersVested Technology
 
AWE USA 2018 Startup Pitch: Alper Guler with Kabaq
AWE USA 2018 Startup Pitch: Alper Guler with KabaqAWE USA 2018 Startup Pitch: Alper Guler with Kabaq
AWE USA 2018 Startup Pitch: Alper Guler with KabaqAugmentedWorldExpo
 
Ruhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Introducing Deep Learning with Keras and PythonRuhr.PY - Introducing Deep Learning with Keras and Python
Ruhr.PY - Introducing Deep Learning with Keras and PythonShirin Elsinghorst
 
ePOM - Intro to Ocean Data Science - Data Visualization
ePOM - Intro to Ocean Data Science - Data VisualizationePOM - Intro to Ocean Data Science - Data Visualization
ePOM - Intro to Ocean Data Science - Data VisualizationGiuseppe Masetti
 
20081101 1000sp
20081101 1000sp20081101 1000sp
20081101 1000spK Yamaguchi
 
RとPythonを比較する
RとPythonを比較するRとPythonを比較する
RとPythonを比較するJoe Suzuki
 
R集会@統数研
R集会@統数研R集会@統数研
R集会@統数研Joe Suzuki
 
E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...Joe Suzuki
 
分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減する分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減するJoe Suzuki
 
連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定Joe Suzuki
 
E-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka UniversityE-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka UniversityJoe Suzuki
 
UAI 2017
UAI 2017UAI 2017
UAI 2017Joe Suzuki
 
AMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップAMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップJoe Suzuki
 
CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要Joe Suzuki
 
A Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent LearningA Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent LearningJoe Suzuki
 
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...Joe Suzuki
 
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...Joe Suzuki
 
研究紹介(学生向け)
研究紹介(学生向け)研究紹介(学生向け)
研究紹介(学生向け)Joe Suzuki
 
Bayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal MeasuresBayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal MeasuresJoe Suzuki
 
MDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/MeasureMDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/MeasureJoe Suzuki
 
The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...Joe Suzuki
 
Universal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or ContinuousUniversal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or ContinuousJoe Suzuki
 
Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Joe Suzuki
 
The Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu AlgorithmThe Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu AlgorithmJoe Suzuki
 
Bayes Independence Test
Bayes Independence TestBayes Independence Test
Bayes Independence TestJoe Suzuki
 
CELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdfCELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdfNistarini College, Purulia (W.B) India
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPirithiRaju
 

More Related Content

More from Joe Suzuki

RとPythonを比較する
RとPythonを比較するRとPythonを比較する
RとPythonを比較するJoe Suzuki
 
R集会@統数研
R集会@統数研R集会@統数研
R集会@統数研Joe Suzuki
 
E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...Joe Suzuki
 
分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減する分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減するJoe Suzuki
 
連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定Joe Suzuki
 
E-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka UniversityE-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka UniversityJoe Suzuki
 
AMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップAMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップJoe Suzuki
 
CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要Joe Suzuki
 
A Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent LearningA Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent LearningJoe Suzuki
 
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...Joe Suzuki
 
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...Joe Suzuki
 
研究紹介(学生向け)
研究紹介(学生向け)研究紹介(学生向け)
研究紹介(学生向け)Joe Suzuki
 
Bayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal MeasuresBayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal MeasuresJoe Suzuki
 
MDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/MeasureMDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/MeasureJoe Suzuki
 
The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...Joe Suzuki
 
Universal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or ContinuousUniversal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or ContinuousJoe Suzuki
 
Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Joe Suzuki
 
The Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu AlgorithmThe Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu AlgorithmJoe Suzuki
 
Bayes Independence Test
Bayes Independence TestBayes Independence Test
Bayes Independence TestJoe Suzuki
 

More from Joe Suzuki (20)

RとPythonを比較する
RとPythonを比較するRとPythonを比較する
RとPythonを比較する
 
R集会@統数研
R集会@統数研R集会@統数研
R集会@統数研
 
E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...E-learning Development of Statistics and in Duex: Practical Approaches and Th...
E-learning Development of Statistics and in Duex: Practical Approaches and Th...
 
分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減する分枝限定法でモデル選択の計算量を低減する
分枝限定法でモデル選択の計算量を低減する
 
連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定連続変量を含む条件付相互情報量の推定
連続変量を含む条件付相互情報量の推定
 
E-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka UniversityE-learning Design and Development for Data Science in Osaka University
E-learning Design and Development for Data Science in Osaka University
 
UAI 2017
UAI 2017UAI 2017
UAI 2017
 
AMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップAMBN2017 サテライトワークショップ
AMBN2017 サテライトワークショップ
 
CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要CRAN Rパッケージ BNSLの概要
CRAN Rパッケージ BNSLの概要
 
A Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent LearningA Conjecture on Strongly Consistent Learning
A Conjecture on Strongly Consistent Learning
 
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
A Generalization of the Chow-Liu Algorithm and its Applications to Artificial...
 
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
A Generalization of Nonparametric Estimation and On-Line Prediction for Stati...
 
研究紹介(学生向け)
研究紹介(学生向け)研究紹介(学生向け)
研究紹介(学生向け)
 
Bayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal MeasuresBayesian Criteria based on Universal Measures
Bayesian Criteria based on Universal Measures
 
MDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/MeasureMDL/Bayesian Criteria based on Universal Coding/Measure
MDL/Bayesian Criteria based on Universal Coding/Measure
 
The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...The Universal Measure for General Sources and its Application to MDL/Bayesian...
The Universal Measure for General Sources and its Application to MDL/Bayesian...
 
Universal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or ContinuousUniversal Prediction without assuming either Discrete or Continuous
Universal Prediction without assuming either Discrete or Continuous
 
Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...Bayesian network structure estimation based on the Bayesian/MDL criteria when...
Bayesian network structure estimation based on the Bayesian/MDL criteria when...
 
The Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu AlgorithmThe Universal Bayesian Chow-Liu Algorithm
The Universal Bayesian Chow-Liu Algorithm
 
Bayes Independence Test
Bayes Independence TestBayes Independence Test
Bayes Independence Test
 

Recently uploaded

Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPirithiRaju
 
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...Sérgio Sacani
 
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPirithiRaju
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​ ​Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​ ​kaibalyasahoo82800
 
Disentangling the origin of chemical differences using GHOST
Disentangling the origin of chemical differences using GHOSTDisentangling the origin of chemical differences using GHOST
Disentangling the origin of chemical differences using GHOSTSérgio Sacani
 
GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)Areesha Ahmad
 
Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...RohitNehra6
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfSumit Kumar yadav
 
GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)Areesha Ahmad
 
Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )aarthirajkumar25
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)Areesha Ahmad
 
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCR
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCRStunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCR
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCRDelhi Call girls
 
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43bNightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43bSérgio Sacani
 
Animal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxAnimal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxUmerFayaz5
 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PPRINCE C P
 
Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)PraveenaKalaiselvan1
 
Botany 4th semester series (krishna).pdf
Botany 4th semester series (krishna).pdfBotany 4th semester series (krishna).pdf
Botany 4th semester series (krishna).pdfSumit Kumar yadav
 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoSérgio Sacani
 

Recently uploaded (20)

CELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdfCELL -Structural and Functional unit of life.pdf
CELL -Structural and Functional unit of life.pdf
 
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdfPests of cotton_Sucking_Pests_Dr.UPR.pdf
Pests of cotton_Sucking_Pests_Dr.UPR.pdf
 
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
PossibleEoarcheanRecordsoftheGeomagneticFieldPreservedintheIsuaSupracrustalBe...
 
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdfPests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf
 
Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​ ​Nanoparticles synthesis and characterization​ ​
Nanoparticles synthesis and characterization​ ​
 
Disentangling the origin of chemical differences using GHOST
Disentangling the origin of chemical differences using GHOSTDisentangling the origin of chemical differences using GHOST
Disentangling the origin of chemical differences using GHOST
 
GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)GBSN - Microbiology (Unit 2)
GBSN - Microbiology (Unit 2)
 
Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...Biopesticide (2).pptx .This slides helps to know the different types of biop...
Biopesticide (2).pptx .This slides helps to know the different types of biop...
 
Botany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdfBotany 4th semester file By Sumit Kumar yadav.pdf
Botany 4th semester file By Sumit Kumar yadav.pdf
 
GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)GBSN - Biochemistry (Unit 1)
GBSN - Biochemistry (Unit 1)
 
Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )Recombination DNA Technology (Nucleic Acid Hybridization )
Recombination DNA Technology (Nucleic Acid Hybridization )
 
GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)GBSN - Microbiology (Unit 1)
GBSN - Microbiology (Unit 1)
 
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCR
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCRStunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCR
Stunning ➥8448380779▻ Call Girls In Panchshil Enclave Delhi NCR
 
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43bNightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
 
Animal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptxAnimal Communication- Auditory and Visual.pptx
Animal Communication- Auditory and Visual.pptx
 
The Philosophy of Science
The Philosophy of ScienceThe Philosophy of Science
The Philosophy of Science
 
VIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C PVIRUSES structure and classification ppt by Dr.Prince C P
VIRUSES structure and classification ppt by Dr.Prince C P
 
Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)Recombinant DNA technology (Immunological screening)
Recombinant DNA technology (Immunological screening)
 
Botany 4th semester series (krishna).pdf
Botany 4th semester series (krishna).pdfBotany 4th semester series (krishna).pdf
Botany 4th semester series (krishna).pdf
 
Isotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on IoIsotopic evidence of long-lived volcanism on Io
Isotopic evidence of long-lived volcanism on Io
 

A Bayesian Approach to Data Compression

  • 1. A Bayesian Approach to Data Compression Joe Suzuki July 17, 2017
  • 2. Road Map PART-I: July 17, 2017 1. Data Compression for Known Source (30 mins) 2. Data Compression for Unknown Source (15 mins) 3. Exercise (45 mins) PART-II: July 24, 2017 Learning a graphical model from data (based on PART-I)