Technical Areas |
Web & Internet Technologies |
Wireless Networking |
Scientific Computing |
Software & System Optimization |
2D & 3D Graphics |
Device Drivers & Hardware Interfaces |
Firmware & BIOS |
Bioinformatics |
Distributed Systems |
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Lepton provides expertise in the modeling of data including:
bioinformatics, time series prediction and financial data
analysis. While our services include traditional linear modeling we
also can provide more sophisticated analysis based on cutting edge techniques
such as Neural Networks, Chaotic Attractor Reconstruction, Hidden Markov
Models and Stochastic Grammars. We thrive on messy and difficult
problems with insufficient information and non-linear relationships.
Lepton's Advantage
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Access to advanced non-linear models. |
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Expertise in the efficient implementation of models. |
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Experience working with financial, biometric and
genetic data. |
Capabilities
- Classical Linear Models
- At Lepton, we can begin with standard techniques such as: Orthogonal
Basis Linear Regression, Markov Modeling, Principal Components
Analysis and Generalized AutoRegressive Conditional Heteroskedasticity
modeling. These models often serve as a good baseline to evaluate the
efficacy of more advanced methods.
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- Advanced Nonlinear Models
- When the data is more intricate, more complex techniques can provide
deeper insights into relationships within data. Lepton can generate
non-linear models based on Hidden Markov Models, Multi-layer Neural
Networks, Chaotic Attractor Reconstruction, and Stochastic Context
Free Grammars. Lepton can also generate custom models that
encompass your expert understanding of the problem domain through the
use of Bayesian Inference. For the most difficult problems, where the
process domain is not well understood, Lepton can also implement
Genetic Co-evolutionary Systems to search very large process model
spaces.
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- Applications of Modeling
- Lepton can also provide expertise in the use of effective
models. Strongly descriptive models can often be leveraged into
predictive models. Predictive models can be employed for data
compression and utility base decision management support.
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