[Paper Review] Finemapping for GWAS
Finemapping for GWAS Variant
Recent Posts
Recent Posts
[Paper Review] Dimensionality Reduction as Probabilistic Inference
Probabilistic Framework to interpret non-probabilistic Algorithms
[Paper Review] Dimensionality Reduction as Probabilistic Inference
Probabilistic Framework to interpret non-probabilistic Algorithms
계속 쓰는 시
계속 쓰는 시
6월의 시
6월에 적은 시
[Spatial Interpolation Theory] Ch 2. Properties of Random Fields
Preliminary about linear prediction
[Spatial Interpolation Theory] Ch 1. Linear Prediction
Preliminary about linear prediction
4월의 시
4월에 적은 시
3월의 시
3월에 적은 시
20대의 마침표.
연작시
2월의 시
2월에 적은 시
[Quantum Data Science] Part 7. Quantum Factoring Algorithm using Schnorr’s algorithm.
Algorithm for factoring large integer.
[Envelopes Theory] Ch 1. Response Envelopes
Preliminary about Vector Space, Multivariate Linear Model and Response Envelopes
1월의 시
1월에 적은 시
12월의 시
12월에 적은 시
11월의 시
11월에 적은 시
10월의 시
10월에 적은 시
9월의 시
9월에 적은 시
8월의 시
8월에 적은 시
7월의 시
7월에 적은 시
6월의 시
6월에 적은 시
5월의 시
5월에 적은 시
[Quantum Data Science] Part 6. Quantum Algorithm for Combinatorial Optimization
Algorithm for finding optima of combinatorial problems
KQC 2022년 여름 인턴 모집 안내
Korea Quantum Computing
[Real Analysis] Ch4. Hilbert Space
Hilbert Space
[Real Analysis] Ch3. Differentiation and Integration
Differentiation
[Random Matrix Theory] Ch2. Stirling’s Formula and Eigenvalue Theory
Stirling’s formula about approximate behavior of permutation and various eigenvalue inequality and equality.
[Real Analysis] Ch2. Integration Theory
Integration Theory
[Random Matrix Theory] Ch1. Probability Theory
Preliminary of random matrix theory.
[Real Analysis] Ch1. Measure Theory
Measure Theory
[Quantum Data Science] Part 5. Quantum Algorithm for Finance
Algorithm for calculating financial portfolio
[Hilbert Space] Introduction to Function Space
Exploring function space with Hilbert Space Framwork
[Paper Review] Sparse Sufficient Dimension Reduction
Sparse Sufficient Dimension Reduction using Penalty
[Experiment] Non-linear Sufficient Dimension Reduction using quantum kernel
plans to build dimension reduction of quantum version
[Quantum Data Science] Part 4. Quantum Kernel Space
Quantum feature space using quantum kernel
[Quantum Data Science] Part 3. Parameterized Quantum Circuit
Parameterized Quantum Circuit and their optimization method
[Paper Review] Sufficient Dimension Folding for Tensorial Data
Sufficient Dimension Folding Theory and Techniques to reduce the dimension of tensorial data.
[Second-Order Method] Part4 - Quasi Newton Method.
Quasi Newton’s Method to accelerate learning.
[Second-Order Method] Part3 - Primal-Dual Interior-Point Methods
Solving convex problem with inequality constraints
[Quantum Data Science] Part 2. Quantum Information Encoding
Encoding method to embed data into quantum states
[Quantum Data Science] Part 1. Introduction to Quantum Theory
Basic Terminology used at the Quantum Theory
[Second-Order Method] Part2 - Barrier Method
Solving convex problem with inequality constraints
[Second-Order Method] Part1 - Newton Raphson Method
Newton Raphson Method
[Basic of Quantum Computer] Part 7. Security System of the Quantum computer and Quantum Protocol
Comparing security of quantum and classical computer and Quantum protocol
[Basic of Quantum Computer] Part 6. Starting Qiskit
Installing qiskit and constructing basic circuit Qiskit
Quantum5_eng
title: [Basic of Quantum Computer] Part 5. Quantum Phase Estimation and Quantum Distance Measure categories: [Quantum] tags: [Quantum Computer] excerpt: Q...
[Basic of Quantum Computer] Part 4. Grover’s Algorithm, Deutsch’s Algorithm
Oracle, Amplitude Amplification, Grover’s and Deutsch’s Algorithm
[Basic of Quantum Computer] Part 3.Complex Number and Quantum Fourier Transformation
Basic concept of the complex number and its usage in the quantum computer]
[Basic of Quantum Computer] Part 2.Quantum Gate and Basic Circuit
basic component of the quantum computer.
[Basic of Quantum Computer ]Part 1.Concept of Quantum Computer and Qubit
Introduction and fundamentals of Quantum computer
[양자 데이터 사이언스] HHL Algorithm과 Hybrid HHL Algorithm
양자컴퓨터를 이용한 행렬 계산법
[Sufficient Dimension Reduction] Part 4. Central Mean Space
Generalized version of SDR and SIR
[Non-linear Dimension Reduction] Part 3. Central sigma-Field for Nonlinear SDR and GSIR
Generalized version of SDR and SIR
[Non-linear Dimension Reduction] Part 2. Coordinate Representation and KPCA
Non-linear Dimension Reduction - 2. Coordinate Representation and KPCA
[Non-linear Dimension Reduction] Part 1. Reproducing Kernel Hilbert Space
Reproducing Kernel Hilbert Space
[Hilbert Space] Linear Operator in Euclidean Space
Advanced concept about Matrix
[RCA] Ch2.Positive Lebesgue measure(2/2)
Positive Lebesgue Measure
[RCA] Ch2.Positive Lebesgue measure(1/2)
Riesz representation theorem
[Sufficient Dimension Reduction] Part3. Contour Regression and Directional Regression
Contour regression and Directional Regression
Linearmixedmodel
title: [Statistic] Linear Mixed Model for correlated data categories: [others] tags: [Others, method] excerpt: panel data, linear mixed model Linear Mi...
[Sufficient Dimension Reduction] Part2. Advanced algorithm of SIR
Parametric, Kernel Inverse regression, Sliced Average Variance Estimator.
[RCA] Ch1.Abstract Integration (2/2)
Absatract Integration , lebesgue integration, measure zero
[RCA] Ch1.Abstract Integration (1/2)
Absatract Integration, sigma-algebra, simple function, measure
[Sufficient Dimension Reduction] Part1. Introduction and Sliced Inverse Regression
Introduction to SDR and SIR method
[양자컴퓨터 기초 이론] 7. RSA 보안체계와 양자 프로토콜
소인수 분해를 기반으로한 현세대 암호이론 기초와 양자컴퓨터의 암호이론 기초
[양자컴퓨터 기초 이론] 6. 키스킷 시작하기
Qiskit 설치 및 기본적인 회로작성
[생성모델] Basic Generative Model Part2 - Regulized AutoEncoder
Sparse Autoencoder, Denoising AutoEncoder, Contractive Autoencoderㅇ
[양자컴퓨터 기초 이론] 5. 그로버 알고리즘과 도이치 알고리즘
오라클, 확률증폭, Grover’s Algorithm과 Deutsch’s Algorithm
[생성모델] Basic Generative Model Part1 - Basic Preview of Generative Model and AutoEncoder
생성모델의 개념과 Basic Autoencoder
[Theory of Optimization] Convex Optimization의 최적화 기법 총정리
Gradient Descent와 Newtown’s Method의 정리
[First-Order Method] Part3. Proximal Gradient Descent
Proximal Gradient Descent and Stochastic Gradient Descent
[양자컴퓨터 기초 이론] 4. 양자 페이즈 추정과 내적 계산법
Quantum Phase Estimation와 Quantum State Distance Measure
[양자컴퓨터 기초 이론] 3.복소수의 기본과 푸리에 변환
복소수에 대한 기본적인 개념과 Quantum Fourier Analysis
[First-Order Method] Part2. Subgradient Method
Subgradient Method
[양자컴퓨터 기초 이론] 2.양자 게이트와 회로
양자 게이트와 이를 이용한 기본적인 회로들
[양자 컴퓨터 기초 이론] 1.양자컴퓨터와 큐빗
양자 컴퓨터 인트로 및 fundamentals
[First-Order Method] Example2. Subgradient method
L1 penalty에 대한 subgradient method 실습
[First-Order Method] Part1. Gradient Descent
Gradient Descent
[First-Order Method] Example1. Basic Gradient Descent
OLS에 대한 Gradient Descent 실습
[Theory of Optimization] Application of Duality
Application of Duality
[Marsden_Analysis] 4. Continuous Mapping - Part A(KOR)
Continuous Function
[Marsden_Analysis] 4. Continuous Mapping - Part A (KOR)
Continuous Function
[Theory of Optimization] convexity and duality
meaning of convex and duality
[Marsden_Analysis] 3. Compact and Connected Set - Part B (KOR)
Nested Set Property,Connected Set
[Basic Dimension Reduction] Part4. Advanced PCA
PCA의 응용 기법에 대한 설명
[Basic Dimension Reduction] Kernel Trick을 활용한 알고리즘 (Polynomial Regression,Kernel PCA,SVM)
Kernel Trick을 활용한 High-Dimension Mapping Computing의 간소화
[Marsden_Analysis] 2. The Topology of Euclidean Space - Part B(KOR)
Boundary Point, Sequence and Completness
[Marsden_Analysis] 2. The Topology of Euclidean Space - Part A (KOR)
Set Theory, Open Set, Closed Set
[Bayesian Inference]히든 마르코프 체인
기본적인 히든 마르코프 체인 구조에 대한 서술
[Marsden_Analysis] 1.The Real Line And Euclidean Space - Part B (KOR)
Least Upper Bounds와 Cauchy Sequence, Cluster Point
[Marsden_Analysis] 1.The Real Line And Euclidean Space - Part A (KOR)
Ordered Field,Number System, Real Number
[Bayesian Inference] Finite Mixture Model 실습
Finite Mixture Model를 이해하고 구현해보기
[Basic Dimension Reduction] Part1. Bregman Divergence
Bregman Divergence에 관한 설명
[Bayesian Inference] VI 공식 적용 및 실습
VI를 통해 다중 회귀분석 적용해보기
[Bayesian Inference] Markov Chain Monete Carlo
MCMC방법인 Gibbs Sampling과 MH 기법에 대한 정리
[Bayesian Inference] GMM 모수 추정 실습
GMM의 모수를 Gibbs Sampler와 EM Algorithm으로 추정해보기
[Bayesian Inference] Variational Inference
PCA,MCA,FA에 대한 대략적인 설명
[Basic Dimension Reduction] 군집분석(Cluster Analysis)
분할기법,계층기법,밀도기법에 대한 설명
[Basic Dimension Reduction] PCA,MCA,FA를 활용한 차원축소
PCA,MCA,FA에 대한 대략적인 설명
[기술]마크다운 사용하기
마크다운 매뉴얼에 대한 서술
[기술]주피터 노트북으로 슬라이드쇼 만들기
nbconvert,rise,reveal.js에 대한 설명
[프로젝트]IGA Works 클릭율 예측 대회 참가 기록
예측 모델링 과정과 개선점에 대한 포스팅