Uncertainty
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portes grátis
Uncertainty
The Soul of Modeling, Probability & Statistics
Briggs, William
Springer International Publishing AG
05/2018
258
Mole
Inglês
9783319819587
15 a 20 dias
4277
Descrição não disponível.
1. Truth, Argument, Realism
1.1. Truth
1.2. Realism
1.3. Epistemology
1.4. Necessary & Conditional Truth
1.5. Science & Scientism
1.6. Faith
1.7. Belief & Knowlege
2. Logic
2.1. Language
2.2. Logic Is Not Empirical
2.3. Syllogistic Logic
2.4. Syllogisms
2.5. Informality
2.6. Fallacy
3. Induction and Intellection
3.1. Metaphysics
3.2. Types of Induction
3.3. Grue
4. What Probability Is
4.1. Probability Is Conditional
4.2. Relevance
4.3. The Proportional Syllogism
4.4. Details
4.5. Assigning Probability
4.6. Weight of Probability
4.7. Probability Usually Is Not a Number
4.8. Probability Can Be a Number
5. What Probability Is Not
5.1. Probability Is Not Physical
5.2. Probability & Essence
5.3. Probability Is Not Subjective
5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux
6. Chance and Randomness
6.1. Randomness
6.2. Not a Cause
6.3. Experimental Design & Randomization
6.4. Nothing Is Distributed
6.5. Quantum Mechanics
6.6. Simulations
6.7. Truly Random & Information Theory
7. Causality
7.1. What Is Cause Like?
7.2. Causal Models
7.3. Paths
7.4. Once a Cause, Always a Cause
7.5. Falsifiability
7.6. Explanation
7.7. Under-Determination
8. Probability Models
8.1. Model Form
8.2. Relevance & Importance
8.3. Independence versus Irrelevance
8.4. Bayes
8.5. The Problem and Origin of Parameters
8.6. Exchangeability and Parameters
8.7. Mystery of Parameters
9. Statistical and Physical Models <
9.1. The Idea
9.2. The Best Model
9.3. Second-Best Models
9.4. Relevance and Importance
9.5. Measurement
9.6. Hypothesis Testing
9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models
9.9. Model Goodness
9.10. Decisions
10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series
10.6. The Future
1.1. Truth
1.2. Realism
1.3. Epistemology
1.4. Necessary & Conditional Truth
1.5. Science & Scientism
1.6. Faith
1.7. Belief & Knowlege
2. Logic
2.1. Language
2.2. Logic Is Not Empirical
2.3. Syllogistic Logic
2.4. Syllogisms
2.5. Informality
2.6. Fallacy
3. Induction and Intellection
3.1. Metaphysics
3.2. Types of Induction
3.3. Grue
4. What Probability Is
4.1. Probability Is Conditional
4.2. Relevance
4.3. The Proportional Syllogism
4.4. Details
4.5. Assigning Probability
4.6. Weight of Probability
4.7. Probability Usually Is Not a Number
4.8. Probability Can Be a Number
5. What Probability Is Not
5.1. Probability Is Not Physical
5.2. Probability & Essence
5.3. Probability Is Not Subjective
5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux
6. Chance and Randomness
6.1. Randomness
6.2. Not a Cause
6.3. Experimental Design & Randomization
6.4. Nothing Is Distributed
6.5. Quantum Mechanics
6.6. Simulations
6.7. Truly Random & Information Theory
7. Causality
7.1. What Is Cause Like?
7.2. Causal Models
7.3. Paths
7.4. Once a Cause, Always a Cause
7.5. Falsifiability
7.6. Explanation
7.7. Under-Determination
8. Probability Models
8.1. Model Form
8.2. Relevance & Importance
8.3. Independence versus Irrelevance
8.4. Bayes
8.5. The Problem and Origin of Parameters
8.6. Exchangeability and Parameters
8.7. Mystery of Parameters
9. Statistical and Physical Models <
9.1. The Idea
9.2. The Best Model
9.3. Second-Best Models
9.4. Relevance and Importance
9.5. Measurement
9.6. Hypothesis Testing
9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models
9.9. Model Goodness
9.10. Decisions
10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series
10.6. The Future
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
probability;statistics;philosophy;cause;evidence;models;modeling;epistemology;philosophy of uncertainty;logic
1. Truth, Argument, Realism
1.1. Truth
1.2. Realism
1.3. Epistemology
1.4. Necessary & Conditional Truth
1.5. Science & Scientism
1.6. Faith
1.7. Belief & Knowlege
2. Logic
2.1. Language
2.2. Logic Is Not Empirical
2.3. Syllogistic Logic
2.4. Syllogisms
2.5. Informality
2.6. Fallacy
3. Induction and Intellection
3.1. Metaphysics
3.2. Types of Induction
3.3. Grue
4. What Probability Is
4.1. Probability Is Conditional
4.2. Relevance
4.3. The Proportional Syllogism
4.4. Details
4.5. Assigning Probability
4.6. Weight of Probability
4.7. Probability Usually Is Not a Number
4.8. Probability Can Be a Number
5. What Probability Is Not
5.1. Probability Is Not Physical
5.2. Probability & Essence
5.3. Probability Is Not Subjective
5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux
6. Chance and Randomness
6.1. Randomness
6.2. Not a Cause
6.3. Experimental Design & Randomization
6.4. Nothing Is Distributed
6.5. Quantum Mechanics
6.6. Simulations
6.7. Truly Random & Information Theory
7. Causality
7.1. What Is Cause Like?
7.2. Causal Models
7.3. Paths
7.4. Once a Cause, Always a Cause
7.5. Falsifiability
7.6. Explanation
7.7. Under-Determination
8. Probability Models
8.1. Model Form
8.2. Relevance & Importance
8.3. Independence versus Irrelevance
8.4. Bayes
8.5. The Problem and Origin of Parameters
8.6. Exchangeability and Parameters
8.7. Mystery of Parameters
9. Statistical and Physical Models <
9.1. The Idea
9.2. The Best Model
9.3. Second-Best Models
9.4. Relevance and Importance
9.5. Measurement
9.6. Hypothesis Testing
9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models
9.9. Model Goodness
9.10. Decisions
10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series
10.6. The Future
1.1. Truth
1.2. Realism
1.3. Epistemology
1.4. Necessary & Conditional Truth
1.5. Science & Scientism
1.6. Faith
1.7. Belief & Knowlege
2. Logic
2.1. Language
2.2. Logic Is Not Empirical
2.3. Syllogistic Logic
2.4. Syllogisms
2.5. Informality
2.6. Fallacy
3. Induction and Intellection
3.1. Metaphysics
3.2. Types of Induction
3.3. Grue
4. What Probability Is
4.1. Probability Is Conditional
4.2. Relevance
4.3. The Proportional Syllogism
4.4. Details
4.5. Assigning Probability
4.6. Weight of Probability
4.7. Probability Usually Is Not a Number
4.8. Probability Can Be a Number
5. What Probability Is Not
5.1. Probability Is Not Physical
5.2. Probability & Essence
5.3. Probability Is Not Subjective
5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux
6. Chance and Randomness
6.1. Randomness
6.2. Not a Cause
6.3. Experimental Design & Randomization
6.4. Nothing Is Distributed
6.5. Quantum Mechanics
6.6. Simulations
6.7. Truly Random & Information Theory
7. Causality
7.1. What Is Cause Like?
7.2. Causal Models
7.3. Paths
7.4. Once a Cause, Always a Cause
7.5. Falsifiability
7.6. Explanation
7.7. Under-Determination
8. Probability Models
8.1. Model Form
8.2. Relevance & Importance
8.3. Independence versus Irrelevance
8.4. Bayes
8.5. The Problem and Origin of Parameters
8.6. Exchangeability and Parameters
8.7. Mystery of Parameters
9. Statistical and Physical Models <
9.1. The Idea
9.2. The Best Model
9.3. Second-Best Models
9.4. Relevance and Importance
9.5. Measurement
9.6. Hypothesis Testing
9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models
9.9. Model Goodness
9.10. Decisions
10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series
10.6. The Future
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
