Dynamic Scoring: Probabilistic Model Selection Based on Utility Maximization

2018 ◽  
Author(s):  
Jan Vecer
Keyword(s):  
Model Selection ◽  
Probabilistic Model ◽  
Utility Maximization ◽  
Dynamic Scoring

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

scholarly journals Duality theory under model uncertainty for non-concave utility functions

2019 ◽  
pp. 50-56
Author(s):  
O. O. Kharytonova
Keyword(s):  
Probabilistic Model ◽  
Model Uncertainty ◽  
Duality Theory ◽  
Utility Maximization ◽  
Utility Functions ◽  
Previous Literature ◽  
Market Models ◽  
Terminal Wealth ◽  
Complete Market ◽  
Robust Utility Maximization

The main goal for this paper is to study the robust utility maximization functional, i.e. sup_{X\in\Xi(x)} inf_{Q\in\mathsf{Q}} E_Q [U(X_T)]; of the terminal wealth in complete market models, when the investor is uncertain about the underlying probabilistic model and averse against both risk and model uncertainty. In the previous literature, this problem was studied for strictly concave utility functions and we extended existing results for non-concave utility functions by considering their concavization.

scholarly journals Dynamic Scoring: Probabilistic Model Selection Based on Utility Maximization

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 36 ◽  
Author(s):  
Jan Vecer
Keyword(s):  
Model Selection ◽  
Trading Volume ◽  
Utility Maximization ◽  
Supply And Demand ◽  
Demand Functions ◽  
Optimal Trading ◽  
Novel Approach ◽  
Probability Estimates ◽  
Betting Market ◽  
Selection For

We propose a novel approach of model selection for probability estimates that may be applied in time evolving setting. Specifically, we show that any discrepancy between different probability estimates opens a possibility to compare them by trading on a hypothetical betting market that trades probabilities. We describe the mechanism of such a market, where agents maximize some utility function which determines the optimal trading volume for given odds. This procedure produces supply and demand functions, that determine the size of the bet as a function of a trading probability. These functions are closed form for the choice of logarithmic and exponential utility functions. Having two probability estimates and the corresponding supply and demand functions, the trade matching these estimates happens at the intersection of the supply and demand functions. We show that an agent using correct probabilities will realize a profit in expectation when trading against any other set of probabilities. The expected profit realized by the correct view of the market probabilities can be used as a measure of information in terms of statistical divergence.

scholarly journals Inferring Tumor Progression in Large Datasets

2020 ◽  
Author(s):  
Mohammadreza Mohaghegh Neyshabouri ◽  
Seong-Hwan Jun ◽  
Jens Lagergren
Keyword(s):  
Model Selection ◽  
Tumor Progression ◽  
Cancer Progression ◽  
Probabilistic Model ◽  
Temporal Order ◽  
Selective Advantage ◽  
Large Datasets ◽  
Superior Performance ◽  
Inference Algorithm ◽  
Driver Genes

AbstractIdentification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression.Author summaryCancer is a disease caused by the accumulation of somatic mutations in the genome. This process is mainly driven by mutations in certain genes that give the harboring cells some selective advantage. The rather few driver genes are usually masked amongst an abundance of so-called passenger mutations. Identification of the driver genes and the temporal order in which the mutations occur is of great importance towards research and clinical objectives. In this paper, we introduce a probabilistic model for cancer progression and devise an efficient inference algorithm to train the model. We show that our method scales favorably to large datasets and provides superior performance compared to an ILP-based counterpart on a wide set of synthetic data simulations. Our Bayesian approach also allows for systematic model selection and confidence quantification procedures in contrast to the previous non-probabilistic progression models. We also study two large datasets on colorectal and glioblastoma cancers and validate our inferred model in comparison to the ILP-based method.

Sign in / Sign up

Export Citation Format

Share Document