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Statistics and Statistical Learning (MMath)
MMath book lists:
00: General Mathematics *
01: History of Mathematics *
03: Logic and Set Theory *
05: Combinatorics and Graph Theory *
06: Order and Lattices
08: General Algebra *
11: Number Theory *
12: Field Theory
13: Commutative Algebra
14: Algebraic Geometry *
15: Linear Algebra *
16: Noncommutative Rings
17: Lie Algebras
18: Category Theory *
19: K-Theory
20: Group Theory *
22: Lie Groups *
26: Real Analysis *
28: Measure and Integration *
30: Complex Analysis *
31: Potential Theory
32: Several Complex Variables
33: Special Functions
34: Ordinary Differential Equations
35: Partial Differential Equations *
37: Dynamical Systems and Ergodic Theory
39: Difference Equations
40: Sequences and Series
41: Approximation Theory
42: Harmonic Analysis *
43: Abstract Harmonic Analysis
44: Integral Transforms
45: Integral Equations
46: Functional Analysis *
47: Operator Theory
49: Calculus of Variations and Optimal Control
51: Geometry *
52: Convex Geometry and Discrete Geometry
53: Differential Geometry *
54: General Topology *
55: Algebraic Topology *
57: Manifolds and Cell Complexes
58: Analysis on Manifolds *
60: Probability and Stochastic Processes *
62: Statistics and Statistical Learning *
65: Numerical Methods *
68: Theoretical Computer Science *
70: Classical Mechanics *
74: Mechanics of Deformable Solids
76: Fluid Dynamics
78: Electrodynamics
80: Thermal Physics
81: Quantum Physics *
82: Statistical Physics *
83: Relativity and Gravitation *
85: Astrophysics
86: Geophysics
90: Operational Research
91: Game Theory, Economics, Finance
92: Mathematical Biology
93: Control and Systems Theory
94: Information Theory and Communication *
97: Mathematics Education *
* indicates a significant topic in undergraduate mathematics
MSC2020 mathematics subject classifications:
https://mathscinet.ams.org/mathscinet...
00: General Mathematics *
01: History of Mathematics *
03: Logic and Set Theory *
05: Combinatorics and Graph Theory *
06: Order and Lattices
08: General Algebra *
11: Number Theory *
12: Field Theory
13: Commutative Algebra
14: Algebraic Geometry *
15: Linear Algebra *
16: Noncommutative Rings
17: Lie Algebras
18: Category Theory *
19: K-Theory
20: Group Theory *
22: Lie Groups *
26: Real Analysis *
28: Measure and Integration *
30: Complex Analysis *
31: Potential Theory
32: Several Complex Variables
33: Special Functions
34: Ordinary Differential Equations
35: Partial Differential Equations *
37: Dynamical Systems and Ergodic Theory
39: Difference Equations
40: Sequences and Series
41: Approximation Theory
42: Harmonic Analysis *
43: Abstract Harmonic Analysis
44: Integral Transforms
45: Integral Equations
46: Functional Analysis *
47: Operator Theory
49: Calculus of Variations and Optimal Control
51: Geometry *
52: Convex Geometry and Discrete Geometry
53: Differential Geometry *
54: General Topology *
55: Algebraic Topology *
57: Manifolds and Cell Complexes
58: Analysis on Manifolds *
60: Probability and Stochastic Processes *
62: Statistics and Statistical Learning *
65: Numerical Methods *
68: Theoretical Computer Science *
70: Classical Mechanics *
74: Mechanics of Deformable Solids
76: Fluid Dynamics
78: Electrodynamics
80: Thermal Physics
81: Quantum Physics *
82: Statistical Physics *
83: Relativity and Gravitation *
85: Astrophysics
86: Geophysics
90: Operational Research
91: Game Theory, Economics, Finance
92: Mathematical Biology
93: Control and Systems Theory
94: Information Theory and Communication *
97: Mathematics Education *
* indicates a significant topic in undergraduate mathematics
MSC2020 mathematics subject classifications:
https://mathscinet.ams.org/mathscinet...
Score
A book’s total score is based on multiple factors, including the number of people who have voted for it and how highly those voters ranked the book.
| 1 |
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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4.43 avg rating — 1,874 ratings
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| 2 |
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Statistical Inference
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| 3 |
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Statistical Rethinking: A Bayesian Course with Examples in R and STAN
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Machine Learning: A Probabilistic Perspective
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| 5 |
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Principles of Statistics (Dover Books on Mathematics)
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| 6 |
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Bayesian Data Analysis
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| 7 |
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An Introduction to Statistical Learning: with Applications in R
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4.59 avg rating — 2,308 ratings
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| 8 |
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Information Theory, Inference, and Learning Algorithms
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4.52 avg rating — 487 ratings
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| 9 |
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The Art of Statistics: Learning from Data
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4.15 avg rating — 5,451 ratings
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| 10 |
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All of Statistics: A Concise Course in Statistical Inference
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4.26 avg rating — 388 ratings
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| 11 |
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Schaum's Outline of Probability and Statistics
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3.80 avg rating — 165 ratings
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| 12 |
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Probability and Statistics (4th Edition)
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3.86 avg rating — 132 ratings
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| 13 |
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Philosophy of Statistics (Volume 7) (Handbook of the Philosophy of Science, Volume 7)
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Prasanta S. Bandyopadhyay (Editor)
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| 13 |
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Topological Data Analysis for Genomics and Evolution: Topology in Biology
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4.27 avg rating — 11 ratings
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| 15 |
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An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
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4.33 avg rating — 67 ratings
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| 16 |
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Foundations of Machine Learning
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4.21 avg rating — 94 ratings
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| 17 |
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A Student’s Guide to Bayesian Statistics
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Ben Lambert (Goodreads Author)
4.48 avg rating — 75 ratings
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| 18 |
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Regression and Other Stories
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4.26 avg rating — 119 ratings
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| 19 |
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The Nature of Statistical Learning Theory
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4.26 avg rating — 34 ratings
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| 19 |
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In All Likelihood: Statistical Modelling and Inference Using Likelihood
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4.29 avg rating — 28 ratings
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| 21 |
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Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs, Series Number 1)
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4.14 avg rating — 22 ratings
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| 22 |
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The Minimum Description Length Principle
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4.08 avg rating — 12 ratings
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| 23 |
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Foundations of Data Science
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4.24 avg rating — 25 ratings
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| 24 |
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Nonlinear Time Series Analysis
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4.39 avg rating — 23 ratings
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| 25 |
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Poisson Processes
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4.44 avg rating — 18 ratings
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| 26 |
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Methods of Information Geometry (Translations of Mathematical Monographs) (v. 191)
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4.50 avg rating — 10 ratings
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| 27 |
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Science and Information Theory
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4.70 avg rating — 10 ratings
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| 28 |
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Numerical Analysis for Statisticians
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4.50 avg rating — 6 ratings
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| 29 |
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Time Series Analysis by State Space Methods
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4.57 avg rating — 7 ratings
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| 30 |
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Algebraic Geometry and Statistical Learning Theory (Cambridge Monographs on Applied and Computational Mathematics, Series Number 25)
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4.38 avg rating — 13 ratings
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| 30 |
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A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics)
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4.40 avg rating — 5 ratings
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| 32 |
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Statistics As Principled Argument
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4.17 avg rating — 107 ratings
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| 33 |
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Optimal Statistical Decisions
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4.50 avg rating — 4 ratings
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| 33 |
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
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4.68 avg rating — 25 ratings
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| 35 |
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Information Geometry and Its Applications (Applied Mathematical Sciences, 194)
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4.57 avg rating — 7 ratings
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| 35 |
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Theoretical Statistics: Topics for a Core Course
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it was amazing 5.00 avg rating — 4 ratings
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| 37 |
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Gaussian Processes for Machine Learning
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4.17 avg rating — 108 ratings
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| 38 |
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Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied and Computational Mathematics, Series Number 24)
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| 38 |
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Information Geometry: Near Randomness and Near Independence (Lecture Notes in Mathematics, 1953)
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4.67 avg rating — 3 ratings
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| 40 |
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Teaching Statistics: A Bag of Tricks
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3.97 avg rating — 69 ratings
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| 41 |
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Schaum's Outline of Introduction to Probability and Statistics
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4.18 avg rating — 56 ratings
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| 42 |
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Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics)
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4.19 avg rating — 36 ratings
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| 43 |
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All of Nonparametric Statistics
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| 44 |
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Kernel Methods for Pattern Analysis
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3.96 avg rating — 28 ratings
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| 45 |
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An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)
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3.64 avg rating — 25 ratings
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| 46 |
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Information Theory and Statistics (Dover Books on Mathematics)
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4.05 avg rating — 20 ratings
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| 47 |
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Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 16)
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4.08 avg rating — 12 ratings
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| 48 |
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Theory of Point Estimation
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| 49 |
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Principles of Statistical Inference
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| 50 |
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Elements of Large-Sample Theory
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| 51 |
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Matrix Algebra: Theory, Computations, and Applications in Statistics
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| 52 |
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Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 11)
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4.30 avg rating — 10 ratings
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| 53 |
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Mathematical Statistics
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4.11 avg rating — 18 ratings
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| 53 |
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Statistics for High-Dimensional Data: Methods, Theory and Applications (Springer Series in Statistics)
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4.20 avg rating — 10 ratings
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| 55 |
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Principal Component Analysis (Springer Series in Statistics)
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4.25 avg rating — 16 ratings
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| 56 |
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Computational Statistics
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3.61 avg rating — 18 ratings
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| 57 |
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Computational Statistics
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| 58 |
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Forecasting, Structural Time Series Models and the Kalman Filter
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4.18 avg rating — 11 ratings
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| 59 |
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Time Series: Theory and Methods: Time Series: Theory and Methods (Springer Series in Statistics)
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| 60 |
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Fundamentals of Nonparametric Bayesian Inference (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 44)
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4.33 avg rating — 3 ratings
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| 61 |
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A Mathematical Theory of Evidence
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| 61 |
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Geometric Modeling in Probability and Statistics
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4.50 avg rating — 2 ratings
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| 63 |
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Probabilistic Graphical Models: Principles and Applications
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4.33 avg rating — 6 ratings
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| 64 |
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Principles of Uncertainty
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| 65 |
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Probability and Statistics for Engineers and Scientists
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4.08 avg rating — 413 ratings
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| 66 |
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Numerical Methods of Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 7)
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| 67 |
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Networks: Optimisation and Evolution (Cambridge Series in Statistical and Probabilistic Mathematics Book 21)
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