Title: Stan: A Statistical Modeling Language and Compiler
Abstract: Stan (http://mc-stan.org/) is a modeling language derived loosely from BUGS/JAGS. Rather than using Gibbs sampling, Stan uses (adaptive) Hamiltonian Monte Carlo sampling, which is implemented using automatic differentiation. Stan is also compiled rather than interpreted and uses a more flexible, imperative language instead of BUGS's declarative graph-specification language. I'll start with the variable declarations and blocks used by Stan, with an emphasis on why they are organized as they are and how the modeling language constructs are converted to code for compilation. Variable types include constrained and unconstrained scalars, vectors, matrices, and arrays; blocks include (transformed) data, (transformed) parameters, model, and generated quantities blocks, corresponding to how variables are used in Bayesian modeling. I'll then discuss how Stan's implemented with automatic differentiation to automatically convert models from constrained to unconstrained parameter spaces and how we use template metaprogramming to implement vectorized probability functions up to a constant. I'll also mention some future applications of our optimization features: maximum marginal likelihood point estimates and stochastic variational approximate Bayesian inference. If anyone's interested, I can also talk about what we've learned about open-source project management as the team and scope grow.
Friday, December 13, 2013
Friday, November 15, 2013
Prof. Yves Atchade (Michigan/Columbia): November 20th
Title: Bayesian inference for doubly intractable distributions.
Abstract: The talk will review some of the recent developments in computational statistics to deal with statistical models with intractable likelihoods (viz. intractable normalizing constants). I will describe in particular our recent methodology that exploits the exact-approximate MCMC framework combined with a Russian roulette trick.
Abstract: The talk will review some of the recent developments in computational statistics to deal with statistical models with intractable likelihoods (viz. intractable normalizing constants). I will describe in particular our recent methodology that exploits the exact-approximate MCMC framework combined with a Russian roulette trick.
Sunday, November 10, 2013
Prof. Dana Pe'er: November 13th
Title: Revealing tumor heterogeneity between and within tumors
Abstract: Systematic characterization of cancer genomes has revealed a staggering complexity and heterogeneity of aberrations among individuals. More recently appreciated that intra-tumor heterogeneity is of critical importance, each tumor harboring sub-populations that vary in clinically important phenotypes such as drug sensitivity. A major challenge involves the development of analysis methods to integrate the flood of high-throughput data on tumors towards a past of personalized care. We will elaborate on two computational approaches on this path: (1) Integration of genetic and genomic data to identify genetic determinants of cancer. (2) Single cell analysis of signaling based on mass cytometry, a novel technology that can accurately measure more than forty signaling molecules simultaneously single cells.
Abstract: Systematic characterization of cancer genomes has revealed a staggering complexity and heterogeneity of aberrations among individuals. More recently appreciated that intra-tumor heterogeneity is of critical importance, each tumor harboring sub-populations that vary in clinically important phenotypes such as drug sensitivity. A major challenge involves the development of analysis methods to integrate the flood of high-throughput data on tumors towards a past of personalized care. We will elaborate on two computational approaches on this path: (1) Integration of genetic and genomic data to identify genetic determinants of cancer. (2) Single cell analysis of signaling based on mass cytometry, a novel technology that can accurately measure more than forty signaling molecules simultaneously single cells.
Sunday, November 3, 2013
Prof. Wei Ji Ma (NYU): November 6th
Abstract: My lab just arrived at NYU (www.cns.nyu.edu/malab). We do human psychophysics, behavioral modeling, and neural modeling. Today, I will be telling three short stories that are still in development:
1) Do humans aspire to optimality or favor simple heuristics in their decision-making? Both notions are prominent in different domains, but it is rare that they can be pitted directly against each other. We do so in a simple, new visual search task.
2) Confidence ratings are widely used in psychophysics, but rarely fitted. In a working memory task in which stimulus estimates and confidence ratings were collected, we tested different mappings from precision to confidence. It seems this mapping is logarithmic.
3) Using forward models of fMRI activity, we are trying to not just decode the stimulus but also uncertainty. A big problem is how to estimate the covariance matrix. I will discuss where we are currently stuck.
1) Do humans aspire to optimality or favor simple heuristics in their decision-making? Both notions are prominent in different domains, but it is rare that they can be pitted directly against each other. We do so in a simple, new visual search task.
2) Confidence ratings are widely used in psychophysics, but rarely fitted. In a working memory task in which stimulus estimates and confidence ratings were collected, we tested different mappings from precision to confidence. It seems this mapping is logarithmic.
3) Using forward models of fMRI activity, we are trying to not just decode the stimulus but also uncertainty. A big problem is how to estimate the covariance matrix. I will discuss where we are currently stuck.
Sunday, October 27, 2013
Mijung Park: Oct 30th
Title: Bayesian learning methods for neural coding.
Abstract: A primary goal in systems neuroscience is to understand how neural spike responses encode information about the external world. A popular approach to this problem is to build an explicit probabilistic model that characterizes the encoding relationship in terms of a cascade of stages: (1) linear dimensionality reduction of a high-dimensional stimulus space using a bank of filters or receptive fields (RFs); (2) a nonlinear function from filter outputs to spike rate; and (3) a stochastic spiking process with recurrent feedback. These models have described single- and multi-neuron spike responses in a wide variety of brain areas.
In this talk, I will present my Ph.D. work that focuses on developing Bayesian methods to efficiently estimate the linear and non-linear stages of the cascade encoding model. First, I will describe a novel Bayesian receptive field estimator based on a hierarchical prior that flexibly incorporates knowledge about the shapes of neural receptive fields. This estimator achieves error rates several times lower than existing methods, and can be applied to a variety of other neural inference problems such as extracting structure in fMRI data. Furthermore, I will present a novel low-rank description of the high dimensional receptive field, combined with a hierarchical prior for more efficient receptive field estimation. Second, I will describe new models for neural nonlinearities using Gaussian processes (GPs) and Bayesian active learning algorithms in ``closed-loop" neurophysiology experiments to rapidly estimate neural nonlinearities. These approaches significantly improve the efficiency of neurophysiology experiments, where data are often limited by the difficulty of maintaining stable recordings from a neuron or neural population.
Abstract: A primary goal in systems neuroscience is to understand how neural spike responses encode information about the external world. A popular approach to this problem is to build an explicit probabilistic model that characterizes the encoding relationship in terms of a cascade of stages: (1) linear dimensionality reduction of a high-dimensional stimulus space using a bank of filters or receptive fields (RFs); (2) a nonlinear function from filter outputs to spike rate; and (3) a stochastic spiking process with recurrent feedback. These models have described single- and multi-neuron spike responses in a wide variety of brain areas.
In this talk, I will present my Ph.D. work that focuses on developing Bayesian methods to efficiently estimate the linear and non-linear stages of the cascade encoding model. First, I will describe a novel Bayesian receptive field estimator based on a hierarchical prior that flexibly incorporates knowledge about the shapes of neural receptive fields. This estimator achieves error rates several times lower than existing methods, and can be applied to a variety of other neural inference problems such as extracting structure in fMRI data. Furthermore, I will present a novel low-rank description of the high dimensional receptive field, combined with a hierarchical prior for more efficient receptive field estimation. Second, I will describe new models for neural nonlinearities using Gaussian processes (GPs) and Bayesian active learning algorithms in ``closed-loop" neurophysiology experiments to rapidly estimate neural nonlinearities. These approaches significantly improve the efficiency of neurophysiology experiments, where data are often limited by the difficulty of maintaining stable recordings from a neuron or neural population.
Saturday, October 19, 2013
Prof. Tian Zheng: Oct 16th
Title: Latent Space Model for Aggregated Relational Data
Abstract: Aggregated Relational Data (ARD) are indirect network data collected using survey questions of the form "how many X's do you know?" It is most often used to estimate the size of populations that are difficult to count directly and allows researchers to choose specific subpopulations of interest without sampling or surveying members of these subpopulations directly. What has been under-utilized is the indirect information on social structure captured by ARD. In this talk, I present a latent space model and Bayesian computation framework for inference and estimation of social structures using ARD from non-network samples in social networks, the variation of social structures in subnetworks, and the relations between (hard-to-reach) subpopulations.
Abstract: Aggregated Relational Data (ARD) are indirect network data collected using survey questions of the form "how many X's do you know?" It is most often used to estimate the size of populations that are difficult to count directly and allows researchers to choose specific subpopulations of interest without sampling or surveying members of these subpopulations directly. What has been under-utilized is the indirect information on social structure captured by ARD. In this talk, I present a latent space model and Bayesian computation framework for inference and estimation of social structures using ARD from non-network samples in social networks, the variation of social structures in subnetworks, and the relations between (hard-to-reach) subpopulations.
Sunday, October 6, 2013
Prof. Rahul Mazumder: Oct 9th
Title: Low-rank Matrix Regularization: Statistical Models and Large Scale Algorithms
Abstract: Low-rank matrix regularization is an important area of research in statistics and machine learning with a wide range of applications --- the task is to estimate X, under a low rank constraint and possibly additional affine (or more general convex) constraints on X. In practice, the matrix dimensions frequently range from hundreds of thousands to even a million --- leading to severe computational challenges. In this talk, I will describe computationally tractable models and scalable (convex) optimization based algorithms for a class of low-rank regularized problems. Exploiting problem-specific statistical insights, problem structure and using novel tools for large scale SVD computations play important roles in this task. I will describe how we can develop a unified, tractable convex optimization framework for general exponential family models, incorporating meta-features on the rows/columns.
Abstract: Low-rank matrix regularization is an important area of research in statistics and machine learning with a wide range of applications --- the task is to estimate X, under a low rank constraint and possibly additional affine (or more general convex) constraints on X. In practice, the matrix dimensions frequently range from hundreds of thousands to even a million --- leading to severe computational challenges. In this talk, I will describe computationally tractable models and scalable (convex) optimization based algorithms for a class of low-rank regularized problems. Exploiting problem-specific statistical insights, problem structure and using novel tools for large scale SVD computations play important roles in this task. I will describe how we can develop a unified, tractable convex optimization framework for general exponential family models, incorporating meta-features on the rows/columns.
Subscribe to:
Posts (Atom)