**Slide 1**

**Can We Generate More Value
from Data?**

**Learning from
Data**

Probabilistic Approach

**Learning from Data**

**From Data to Probability**

**Probabilistic Data Mining**

**What Makes Up ‘Problem
Dimensionality’?**

**Addressing
Dimensionality**

Macroscopic Prediction

**Macroscopic Prediction**

**Boltzmann’s Solution (1877)**

**Boltzmann’s Solution (cont.)**

**Boltzmann’s Solution (cont.)**

**Why Does It Work?**

**Why Does It Work? (cont.)**

**General Maximum Entropy**

**General Maximum Entropy
(cont.)**

**Addressing
Dimensionality**

Parametric Approximation

**Probability Approximation**

**Maximum Likelihood**

**Maximum Likelihood (cont.)**

**Addressing
Dimensionality**

Information Geometry

**Dual Projections**

**Pythagorean Geometry**

**Dual Geometry**

**Bayesian Estimation**

**Addressing
Dimensionality**

Relevance-Based Weighting

**What If the Model Is Too
Complex?**

**Relevance-Based Weighting of
Data**

**Local Empirical
Distributions**

**Local Modeling**

**Multiple Forecasting
Applications**

**Data-Centric Technology**

**Increasingly Popular
Approach**

**How Do Humans Solve
Problems?**

**Corresponding Technologies**

**Pros and Cons**

**Addressing
Dimensionality**

No Locality in High Dimension?

**Limits of Local Modeling**

**No “Local” Data in High
Dimensions**

**Local Modeling Revisited**

**Local Modeling Revisited**

**Cube Encoding**

**Cube Encoding**

**Cube Encoding**

**Cube Encoding**

**General “Linear” Case**

**Symbolic Forecasting**

**Symbolic Forecasting**

**Decision-Making
Process**

Lessons Learnt

**Hypothesis Formulation …**

**Feature Selection …**

**Training Data Selection …**

**Decision Support Rather Than
Automation**

**Humans To Stay in Control**