Bayesian Inference Techniques Research Articles

Impact-force identification using deep learning and Bayesian inference with application on pipeline structures

Structures like bridges and pipelines are vulnerable to impacts from various sources, such as falling debris, over-height vehicles, or floating objects, which can compromise their integrity. Traditional inspection methods are costly and complex and can lead to economic losses due to shutdowns. This research introduces a probabilistic and more efficient approach by combining deep learning and Bayesian inference techniques to accurately measure and analyze these impacts, aiming to enhance the longevity and safety of these structures while mitigating potential critical failures. In this methodology, a novel two-stage approach is implemented to resolve the inverse problem of identifying impact forces on structures. Initially, a convolutional neural network (CNN) classifier for each of the four sensors is employed to determine the impacting material (aluminum, rubber, plastic). This classification stage utilizes ground truth data to identify the nature of the impact accurately. Following this, the preclassified data, categorized by the actual impacting materials, is directed into one of three 5-layer artificial neural networks (ANNs), each designated for a different impacting material. The ANNs act as surrogate models for Bayesian inference to determine impact force and position, resulting in 12 specialized models, each corresponding to a specific combination of sensor and tip type. The ANNs were evaluated using mean squared error, showing precise predictions of the pipeline’s acceleration frequency signals, while the CNN classifier achieved over 99% F1 scores. Using ANN and CNN results, the approximate Bayesian computation with subset simulation technique showed over 90% precision with 7% uncertainty in inferring impact force and 92% precision with 2% uncertainty in determining the impact position along the pipe. Data fusion, combining sensor responses, further improved precision and reduced uncertainty, achieving over 92% precision with 8% uncertainty for impact force and over 98% precision with 1.8% uncertainty for depth. These results demonstrate the method’s reliability and effectiveness in accurately identifying impact forces and locations, even with a single distant sensor.

Instrument Selection in Panel Data Models with Endogeneity: A Bayesian Approach

This paper proposes the use of Bayesian inference techniques to search for and obtain valid instruments in dynamic panel data models where endogenous variables may exist. The use of Principal Component Analysis (PCA) allows for obtaining a reduced number of instruments in comparison to the high number of instruments commonly used in the literature, and Monte Carlo Markov Chain (MCMC) methods enable efficient exploration of the instrument space, deriving accurate point estimates of the elements of interest. The proposed methodology is illustrated in a simulated case and in an empirical application, where the partial effect of a series of determinants on the attraction of international bank flows is quantified. The results highlight the importance of promoting and developing the private sector in these economies, as well as the importance of maintaining good levels of creditworthiness.

Inferring the distribution of the ionising photon escape fraction

Context. The escape fraction of ionising photons from galaxies (fesc) is a key parameter for understanding how intergalactic hydrogen became reionised, but it remains mostly unconstrained. Measurements have been limited to the average value in galaxy ensembles and to handfuls of individual detections. Aims. To help understand which mechanisms govern ionising photon escape, here we infer the distribution of fesc. Methods. We developed a hierarchical Bayesian inference technique to estimate the population distribution of fesc from the ratio of Lyman continuum to non-ionising UV flux measured from broadband photometry. We applied it to a sample of 148 z ≃ 3.5 star-forming galaxies from the VANDELS spectroscopic survey. Results. We explored four physically motivated distributions: constant, log-normal, exponential, and bimodal, and recovered ⟨fesc⟩≈5% for most models. We find the observations are best described by an exponential fesc distribution with scale factor μ =0.05−0.02+0.01. This indicates most galaxies in our sample exhibit very low escape fractions, while predicting substantial ionising photon leakage for only a few galaxies, implying a range of optical depths in the interstellar medium and/or time variability in ionising photon escape. We rule out a bimodal distribution at high significance, indicating that a purely bimodal model of ionising photon escape (due to very strong sightline and/or time variability) is not favoured. We compare our recovered exponential distribution with the SPHINX simulations and find that, while the simulation also predicts an exponential distribution, it significantly underpredicts our inferred mean. The distribution of fesc can be a vital test for simulations in understanding ionising photon leakage, and is important to consider to gain a complete picture of reionisation.

Research on collaborative reconstruction method of missing data and abnormal data in air handling units (AHU)system

The Air Handling Units (AHU), as critical components in building energy management, demand rigorous performance monitoring to ensure indoor comfort and energy efficiency. However, prolonged sensor operation often leads to malfunctions and measurement drifts due to insufficient maintenance, resulting in data gaps and anomalies. These issues pose significant challenges to system operational efficiency, occupant comfort, and energy consumption regulation. To address this dilemma, this paper innovatively leverages Bayesian inference techniques, developing a sophisticated likelihood function distance model. This model achieves precise imputation of missing data and effective rectification of anomalous data, thereby mitigating the issue of error accumulation associated with frequent reliance on historical data.Furthermore, the study exploits the intricate coupling relationships among multiple physical variables within the AHU systems to construct a comprehensive analytical model. This approach reduces the dependence on historical data for mathematical fitting and enhances the reliability of predicted data. Consequently, this paper presents an efficient methodology for handling missing and anomalous sensor data in AHU systems. Experimental results demonstrate that the relative error for reconstructing single-variable missing data remains below 7 %, while the relative error for reconstructing multi-variable missing data is even lower, at 3 %. These findings affirm the practical applicability of the methodology in calibrating anomalous sensor data within the building energy management domain.

Modeling human decomposition: A Bayesian approach

Environmental and individualistic variables affect the rate of human decomposition in complex ways. These effects complicate the estimation of the postmortem interval (PMI) based on observed decomposition characteristics. In this work, we develop a generative probabilistic model for decomposing human remains based on PMI and a wide range of environmental and individualistic variables. This model explicitly represents the effect of each variable, including PMI, on the appearance of each decomposition characteristic, allowing for direct interpretation of model effects and enabling the use of the model for PMI inference and optimal experimental design. In addition, the probabilistic nature of the model allows for the integration of expert knowledge in the form of prior distributions. We fit this model to a diverse set of 2529 cases from the GeoFOR dataset. We demonstrate that the model accurately predicts 24 decomposition characteristics with an ROC AUC score of 0.85. Using Bayesian inference techniques, we invert the decomposition model to predict PMI as a function of the observed decomposition characteristics and environmental and individualistic variables, producing an R-squared measure of 71 %. Finally, we demonstrate how to use the fitted model to design future experiments that maximize the expected amount of new information about the mechanisms of decomposition using the Expected Information Gain formalism.

Precarity and political protest

How does precarity in the labour market affect political engagement? Considering the negative impact of unemployment and atypical employment on turnout, existing research suggests that precarity depresses political participation. However, participation through voting is dependent on supply-side factors and might be an insufficient indicator of engagement with politics. To improve our understanding of this matter, this article investigates the relationship between precarity and protest behaviour, as well as the moderating impact of individual and contextual factors on this relationship. The analysis relies on survey data from thirteen Western European democracies and on a novel operationalisation of precarity that, through the implementation of Bayesian inference techniques on EU-LFS data, allows the capturing of individuals’ vulnerability in the labour market. The results reveal that precarity increases the propensity to protest, exposing the mobilisation potential of precarious workers and suggesting that the representation of this expanding social group might be beneficial for vote-maximizing parties.

Bayesian inverse inference of material properties from microstructure images

In this paper, we introduce a Bayesian framework designed for inverse inference, aiming to predict material properties/process parameters from microstructure images. The integration of Bayesian inference techniques with deep generative models establishes a robust tool for applications in materials science, particularly in material characterization and property control. This integration provides a novel approach to clarifying the reliability of predictions. The application of this framework to a sample problem involving the prediction of material properties from artificial dual-phase steel microstructures demonstrates its capability to estimate these properties while accounting for prediction uncertainties. Moreover, even in comparison to conventional regression methods in terms of point estimation, the proposed framework exhibits superior accuracy in prediction. These results clearly illustrate that the framework presented in this paper constitutes a powerful tool for achieving efficient material design.

Improving Marvel Hero Classification through Dataset Curation

lores the development of a computer vision model for classifying Marvel superheroes such as Black Widow, Hulk, Iron Man, and Spider-Man. Utilizing a curated dataset sourced from Kaggle, the research emphasizes the critical role of dataset quality in refining model accuracy. Insights gained include adjustments to neural network configurations and leveraging Edge Impulse for enhanced performance. The findings highlight effective strategies for optimizing classification accuracy in complex image recognition tasks.PART 2:This part explores the application of Bayesian logistic regression to model the relationship between temperature and the probability of O-ring failure. By leveraging Bayesian inference techniques, analyzing historical data to quantify the risk associated with temperature variations and emphasize the importance of probabilistic approaches in safety-critical decision-making.The Space Shuttle Challenger disaster on January 28, 1986, remains a poignant case study in aerospace engineering failure. The investigation concluded that the failure of O-ring seals in cold temperatures led to the tragic loss of the shuttle and its crew.

Bayesian-informed fatigue life prediction in shallow shell structures with the dual boundary element method

This study introduces an innovative approach for statistically inferring the Equivalent Initial Flaw Size Distribution (EIFSD) in shallow shell structures with the aim of predicting fatigue life. The proposed approach integrates Bayesian inference and surrogate modelling techniques, and utilizes a long crack growth model. The EIFSD is crucial for estimating fatigue life and ensuring structural durability. To showcase the proposed methodology, a numerical example featuring a fuselage window structure under multi-axial loading, employing the Dual Boundary Element Method (DBEM) for crack growth modelling, is presented. Efficiency gains in the inference of the EIFSD are achieved through the development of surrogate models, notably a Co-Kriging model that significantly reduces computational time and outperforms a Kriging model in error reduction. The study explores two assumptions for inspection data sourcing, leading to enhanced methodological effectiveness across varying uncertainty levels. It considers the separate effects of Paris Law constants and loading conditions under increased uncertainty, presenting cases that demonstrate the Co-Kriging model’s high accuracy and computational efficiency. The proposed methodology’s robustness is further validated by assessing the impact of prior distribution quality on Bayesian inference, showing the method’s adaptability to high uncertainty while maintaining a good level of accuracy. This study also demonstrates an approach for fatigue life estimation of structures using the inferred EIFSD. By evaluating the structure up to a critical crack length, the methodology allows for the accurate estimation of fatigue life, which, in turn, facilitates the determination of optimal inspection intervals. Notably, under conditions of high uncertainty, the method showcased a maximum difference of 1.51% in the estimated fatigue life when compared to the true EIFSD, underscoring the technique’s high accuracy and reliability.

Characterization of the Unarmored Dinoflagellate Karlodinium decipiens (Dinophyceae) from Jiaozhou Bay, China

The dinoflagellate genus Karlodinium J. Larsen is well known to form harmful algal blooms (HABs), some of which can produce karlotoxins or other ichthyotoxins and thus cause fish-killing events. Among the 16 currently accepted species of Karlodinium (about half of which are reported to be toxic), six species (K. australe, K. decipiens, K. digitatum, K. elegans, K. veneficum, and K. zhouanum) have been reported or described in the coastal waters of China. However, a fine morphological and molecular characterization of the seldom-observed species K. decipiens has not been conducted; moreover, the negative effects of this species on aquatic animals have not been investigated. This work reports the morphological and phylogenetic characterization of a strain of K. decipiens isolated from Jiaozhou Bay, China, in 2019. The characterization of the strain was conducted using light and scanning electron microscopy, LSU, SSU rDNA, and ITS sequences-based systematic analyses, pigment analysis, and a detailed investigation of its potential toxic/harmful activity on aquatic animals. We observed the typical diagnostic features of K. decipiens, including its relatively large size, ellipsoidal or ovoid cell shape, ventral pore, ventral ridge connecting the two displaced ends of the cingulum, cingulum with a displacement of about one-third of the cell length, numerous polyhedral or slightly elongated chloroplasts distributed peripherally, and large nucleus located centrally. However, we also observed a large amphiesmal vesicle at the dorsal end of the ASC at the dorsal epicone, which is a novel feature that has never been reported from any species of the genus. Based on the results of this study, it is not clear whether this feature is a specific structure of the species or a common characteristic of the genus; therefore, this novel feature is worthy of further examination. Fucoxanthin was the most abundant pigment among all the carotenoids detected. The phylogenies inferred using Bayesian inference (BI) and maximum likelihood (ML) techniques confirmed the conspecificity of our isolate with the holotype K. decipiens (accession no. EF469236). In molecular trees, K. decipiens and K. antarcticum form a separate clade from other species of Karlodinium, and it should be examined whether a large amphiesma vesicle may be a characteristic of this clade. The exposure bioassays using brine shrimp (Artemia salina) indicated that K. decipiens exhibited toxicity to zooplankton, with 100% and 68% mortality observed in brine shrimp using live cell cultures and cell culture lysates over 120 h, respectively. Our work provides a detailed morphological and molecular characterization of K. decipiens from China. The results of this study broaden the known geographical distribution of this species and demonstrate it to be a harmful dinoflagellate.

An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT

BackgroundThe identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence.MethodsA total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI).ResultsDirect lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3–66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs.ConclusionThe interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.

Applying Machine Learning Techniques: Uncertainty Quantification in Nonlinear Dynamics Characters Predictions via Gated Recurrent Unit-Based Reduced-Order Models

The development of reduced-order models has been a pivotal advancement in the computational analysis of fluid dynamics, substantially simplifying the complexity and boosting the efficiency of simulations. The accuracy and practicality of these models largely depend on the reduction techniques applied and the inherent characteristics of the fluid dynamics systems they represent. In this paper, we introduce an innovative machine-learning framework for assessing model uncertainty in computationally intensive reduced-order models. By combining subspace construction methods with advanced Bayesian inference techniques, our approach effectively captures the posterior distribution of model parameters, thereby providing an accurate representation of uncertainty in aerodynamic performance predictions. We employ the NACA0012 airfoil as a case study to validate our method’s ability to enhance the efficiency of reduced-order models and precisely measure the uncertainty inherent in predictions made by recurrent neural networks. It is important to note that our approach is influenced by specific constraints and variables that significantly impact the mean and variability of the predicted final lift coefficient distribution. Our findings indicate that setting the goodness of fit (R2) threshold above 0.985 markedly improves the correlation between Computational Fluid Dynamics (CFD) outcomes and model predictions, increasing from 72.2% to 97.9% as the interval amplification factor adjusts from 1.5 to 3. However, this adjustment causes a considerable expansion of the confidence interval, from 0.0737 to 0.1282, an increase of over 70%. Despite these challenges, our machine learning-based methodology provides essential insights into the further development of reduced-order modeling and uncertainty quantification in fluid dynamics. This highlights the need for ongoing research into the model parameters, especially in applications concerning aircraft control systems, to meet design specifications and ensure system reliability.

Inferring the spin distribution of binary black holes using deep learning

The spin characteristics of black holes offer valuable insights into the evolutionary pathways of their progenitor stars. This is crucial for understanding the broader population properties of black holes. Traditional hierarchical Bayesian inference techniques employed to discern these properties often demand substantial time, and consensus regarding the spin distribution of binary black hole (BBH) systems remains elusive. In this study, leveraging observations from GWTC-3, we adopted a machine learning approach to infer the spin distribution of black holes within BBH systems. Specifically, we developed a deep neural network (DNN) and trained it using data generated from a Beta distribution. Our training strategy, involving the segregation of data into 10 bins, not only expedites model training but also enhances the versatility and adaptability of the DNN to accommodate the growing volume of gravitational wave observations. Utilizing Monte Carlo-bootstrap (MC-bootstrap) to generate observation-simulated samples, we derived spin distribution parameters: for the larger BH sample and for the smaller BH sample. Within our constraints, the distributions of component spin magnitudes suggest the likelihood of both black holes in the BBH merger possessing non-zero spin.

A System-Level Reliability Growth Model for Efficient Inspection Planning of Offshore Wind Farms

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.

Bayesian interface technique-based inverse estimation of closure coefficients of standard k−ϵ turbulence model by limiting the number of DNS data points for flow over a periodic hill

Among different numerical methods for modeling turbulent flow, Reynolds-averaged Navier–Stokes (RANS) is the most commonly used and computationally reasonable. However, the accuracy of RANS is lower than that of other high-fidelity numerical methods. In this work, the uncertainties associated with the coefficients of the standard k−ϵ RANS turbulence model are estimated and calibrated to improve the accuracy. The calibration is performed by considering the coefficients individually as well as collectively. The first three coefficients of the standard k−ϵ turbulence model are calibrated among the five coefficients ( Cμ,Cϵ1,Cϵ2,σϵ and σ k ). The Bayesian inference technique using the Metropolis–Hastings algorithm is applied to quantify uncertainties and calibration. Flow over a periodic hill is selected as a test case. The separation height of the bubble at x/h=2 and x/h=4 , along with the streamwise velocity at various locations, has been chosen as the quantities of interest for comparing the results with DNS. The calibration is performed using known high-fidelity data (direct numerical simulation) from the available data set. The velocity field is re-calculated from the calibrated closure coefficients and compared with the same calculated with the standard coefficients of k−ϵ turbulence model (baseline). The deviation of calibrated C µ is almost 50%–60% from baseline and for Cϵ1 and Cϵ2 it is 3%–12% and 6%–9% respectively. The algorithm is tested for different Reynold numbers and data points. A sensitivity analysis is also performed.

High‐Frequency Ground Motions of Earthquakes Correlate With Fault Network Complexity

AbstractUnderstanding the generation of damaging, high‐frequency ground motions during earthquakes is essential both for fundamental science and for effective hazard preparation. Various theories exist regarding the origin of high‐frequency ground motions, including the standard paradigm linked to slip heterogeneity on the rupture plane, and alternative perspectives associated with fault complexity. To assess these competing hypotheses, we measure ground motion amplitudes in different frequency bands for 3 ≤ M ≤ 5.8 earthquakes in Southern California and compare them to empirical ground motion models. We utilize a Bayesian inference technique called the Integrated Nested Laplace Approximation (INLA) to identify earthquake source regions that produce higher or lower ground motions than expected. Our analysis reveals a strong correlation between fault complexity measurements and the high‐frequency ground motion event terms identified by INLA. These findings suggest that earthquakes on complex faults (or fault networks) lead to stronger‐than‐expected ground motions at high frequencies.

Synthetic wind speed time series generation by dynamic factor model

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Analyzing the Impact of Control Strategies on VisceralLeishmaniasis: A Mathematical Modeling Perspective

Visceral Leishmaniasis remains a significant public health challenge in eastern Sudan despite extensive control efforts. This study employs MCMC Bayesian inference techniques to fit a mathematical model for studying visceral Leishmaniasis transmission dynamics with interventions. The research focuses on evaluation of control programs to adapt to the dynamic nature of visceralLeishmaniasis transmission. We analyze 22 years of cumulative visceral Leishmaniasis cases from eastern Sudan, an endemic region for visceral Leishmaniasis, and assessed the effectiveness of interventions implemented thus far. The results reveal that visceral Leishmaniasis is prevalent, with reported cases representing less than 20% of community infections. Improved surveillance and diagnostics are necessary for accurately estimating the disease burden. The effectiveness of intervention strategies for vector control for reducing VL transmission in the region is found to be limited. Furthermore, the model predicts visceral Leishmaniasis cases will continue to increase in the near future, underscoring the need adaptable initiatives to reduce the disease burden.

Bayesian inference of electron density and ion temperature profiles from neutral beam and halo Balmer-α emission at Wendelstein 7-X

By employing Bayesian inference techniques, the full electron density profile from the plasma core to the edge of Wendelstein 7-X (W7-X) is inferred solely from neutral hydrogen beam and halo Balmer-α (Hα) emission data. The halo is a cloud of neutrals forming in the vicinity of the injected neutral beam due to multiple charge exchange reactions. W7-X is equipped with several neutral hydrogen beam heating sources and an Hα spectroscopy system that views these sources from different angles and penetration depths in the plasma. As the beam and halo emission form complex spectra for each spatial point that are non-linearly dependent on the plasma density profile and other parameters, a complete model from the neutral beam injection and halo formation through to the spectroscopic measurements is required. The model is used here to infer electron density profiles for a range of common W7-X plasma scenarios. The inferred profiles show good agreement with profiles determined by the Thomson scattering and interferometry diagnostics across a broad range of absolute densities without any changes to the input or fitting parameters. The time evolution of the density profile in a discharge with continuous core density peaking is successfully reconstructed, demonstrating sufficient spatial resolution to infer strongly shaped profiles. Furthermore, it is shown as a proof of concept that the model is also able to infer the main ion temperature profile using the same data set.

Abstract SY02-03: Stochastic modeling of clonal evolution in carcinogenesis

Abstract In order to develop strategies that can detect cancer earlier, we need to better understand the dynamics of cancer evolution during the human lifetime. Starting from gestation, this includes measuring both the timing of early somatic mutations that lead to clonal mosaicism as well as the growth rates of mutated clones that carry higher potential for malignant transformation. Continuous observation of this evolving clonal composition across a lifetime is not feasible, therefore mathematical models are needed to integrate data from longitudinal and cross-sectional samples and infer these dynamics. Stochastic models of clonal evolution offer such a framework and provide a means for predicting individual clone trajectories into the future. Furthermore, if measurements of evolution are to be utilized clinically for early detection and patient risk stratification, faster methods to apply these models to data are required. To this end, we derived methods using coalescent theory for single-cell DNA sequencing data that enable instantaneous estimation of the growth rate of a clone along with its confidence intervals. Our tool is available in an open source R package, cloneRate, and provides the first method for constructing valid confidence intervals of growth rates analytically without having to rely on Bayesian inference techniques and complex simulations. When applying our methods to recent datasets derived from normal and neoplastic human blood cells, we quantified increased fitness effects of multi-hit driver mutations and found that higher initial clone growth rates led to decreased time to cancer diagnosis in patients. We can also use these models to estimate other important biological parameters that are difficult to measure in vivo such as bounds for total number of hematopoietic stem cells and expansion rates in early development. Ultimately, the ability to easily and quickly parameterize clonal dynamics in individual patients will benefit future early detection efforts and screening strategies for many cancer types. Citation Format: Kit Curtius. Stochastic modeling of clonal evolution in carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY02-03.