# SRN – A Framework for Adaptive MCMC Targeting Multimodal Distribution by Pompe et al.

After a very long break, I come back to the Sunday Reading Notes and write about the paper “A Frame for Adaptive MCMC Targeting Multimodal Distributions” by Emilia Pompe et al. This paper proposes a Monte Carlo method to sample from multimodal distributions. The “divide and conquer” idea separates the sampling task into (1) identifying the modes, (2) sample efficiently within modes and (3) moving between the modes. While the paper emphasizes more heavily on addressing task (2) and (3) and establishing ergodicity , it provides a framework that unites all three tasks.

The main algorithm in this paper is Jumping Adaptive Multimodal Sampler (JAMS). It has a burn-in algorithm that uses optimization-based procedure to identify modes, to merge them and to estimate covariance matrices around the modes. After task (1) is solved by the burn-in algorithm, JAMS alternates between the later two tasks with local moves that samples around a mode and jump moves that facilitate crossing the low density regions. The local moves are adaptive in the sense that it relies on local kernels with parameters that are learned on the fly. The jump moves propose a new mode and choose a new point around the new mode.

This paper proves ergodicity of JAMS by considering it as a case of a general class of Auxiliary Variable Adaptive MCMC. It also provides extensive simulation studies on multivariate Gaussian (d = 200) , mixtures of t-distributions and bananas and a Bayesian model for sensor network localisation. The results are compared against Adaptive Parallel Tempering (APT). In all the cases tested, JAMS out performs APT in terms of root mean squared error scaled by dimension of the problem. JAMS is also more robust to initialization in term of recovering all the modes.

I usually think of Monte Carlo integration and optimization as very similar tasks and even neglect the area of optimization. This paper is a wake up call for me, as I believe the current JAMS algorithm relies on a successful mode identifying stage. The discussions in Section 6 also suggest mode finding as the bottle neck.

# SRN – Bayesian Calibration of Microsimulation Models by Rutter et al.

The Sunday Reading Notes paper for this week is ‘Bayesian Calibration of Microsimulation Models’ by Carolyn Rutter, Diana Miglioretti and James Savarino. This is a 2009 JASA Applications and Case Studies paper.

According to a 2012 Review paper by Rutter et al,

Microsimulation models (MSMs) for health outcomes simulate individual event histories associated with key components of a disease process; these simulated life histories can be aggregated to estimate population-level effects of treatment on disease outcomes and the comparative effectiveness of treatments. Although MSMs are used to address a wide range of research questions, methodological improvements in MSM approaches have been slowed by the lack of communication among modelers. In addition, there are few resources to guide individuals who may wish to use MSM projections to inform decisions.

In this paper, the authors propose a Bayesian method to calibrate microsimulations models, using Markov Chain Monte Carlo. The case study in this paper is the history of colorectal cancer (CRC). In this paper, the authors assume all CRCs arise from an adenoma and the history of CRC consists of four components: 1) adenoma risk, 2) adenoma growth, 3) transition from adenoma to preclinical cancer and 4) transition from preclinical cancel to clinical cancer. These four components are not observed directly and the calibration data consists of prevalence of adenomas and preclinical cancers and the size and/or number of adenomas from many (independent) studies from different years and about different subpopulations and using different colonoscopy methods.

The authors use $\theta$ to denote MSM parameters and these parameters can be separated from $K$ components with independent priors: $\pi(\theta) = \prod_{i=1}^k \pi_i(\theta_i).$ The calibration data come from $N$ independent sources and each follows some distribution  $y_j|\theta \sim f_j(g_j(\theta))$ parametrized by some unknown functions of the MSM parameters. The likelihood is therefore $f(Y | \theta) = f(Y|g(\theta) )= \prod_{j=1}^n f_j(y_j|g_j(\theta))$.

What makes the calibration problem hard is the unknown function $g(\theta)$. Suppose we want to simulate from the posterior distribution of $\theta|Y$ using a Metropolis-Hastings(MH) algorithm, we need to know $r(\theta,\theta^*) = \frac{\pi(\theta)f(y|g(\theta)}{\pi(\theta^*)f(y|g(\theta^*)}$. But because $g$ is unknown, the MH step cannot be performed.

So the authors propose an approximate MH algorithm that includes a step to estimate $g(\theta)$ for both the current value $\theta$ and the proposed value $\theta^*$. To me this feels like an ‘EM’ step: simulate $m$ copies of $\tilde{y}_{ji}$ from $f_j(y_j| g_j(\theta))$ and calculate the MLE $\hat{g}_j(\theta).$ With this approximation, the resulting transition probability function $\hat{\alpha}(\theta,\theta^*)$for the Metropolis-within-Gibbs step on $\theta_i$ is based on $\frac{\pi(\theta_i)}{\pi(\theta_i^*)}\cdot \frac{\prod_{j=1}^N f_j(y_j|\hat{g}_j(\theta^*_i, \theta_{(-i)})}{\prod_{j=1}^n f_j(y_j|\hat{g}_j(\theta))}.$ In the Appendix, the authors prove that this approximation satisfies the detailed-balanced condition in the limit of $m$ goes to infinity.

I think this paper provides an interesting example of how to incorporate data from multiple sources. As the authors point out,

how closely the model should calibrate to observed data is unclear, especially when calibration data are variable and many provide conflicting interest. […] It depends on how modelers trade-off concerns about possibly overparameterizing and overfitting calibration data relative to the importance of exactly replicating observed or expected results.

References:

• Rutter, C. M., Miglioretti, D. L., & Savarino, J. E. (2009). Bayesian calibration of microsimulation models. Journal of the American Statistical Association104(488), 1338-1350.
• Rutter, C. M., Zaslavsky, A. M., & Feuer, E. J. (2011). Dynamic microsimulation models for health outcomes: a review. Medical Decision Making31(1), 10-18.

# SRN – A Geometric Interpretation of the Metropolis-Hastings Algorithm by Billera and Diaconis

Coming back to the Sunday Reading Notes, this week I discuss the paper ‘A Geometric Interpretation of the Metropolis-Hastings Algorithm’ by Louis J. Billera and Persi Diaconis from Statistical Science. This paper is suggested to me by Joe Blitzstein.

In Section 4 of ‘Informed proposals for local MCMC in discrete spaces’ by Giacomo Zanella (see my SRN Part I and II), Zanella mentions that the Metropolis-Hasting acceptance probability function(APF) $\min\left(1,\frac{\pi(y)p(x,y)}{\pi(x)p(y,x)}\right)$ is not the only APF that makes the resulting kernel $\pi$-reversible as long as detailed-balance is satisfied. This comes first as a ‘surprise’ to me as I have never seen another APF in practice. But very quickly I realize that this fact was mentioned in both Stat 213 & Stat 220 at Harvard and I have read about it from Section 5.3 – ‘Why Does the Metropolis Algorithm Work?‘ of ‘Monte Carlo Strategies in Scientific Computing‘ by Jun S. Liu. Unfortunately, I did not pay enough attention. Joe suggested this article to me after I posted on Facebook about being upset with not knowing such a basic fact.

In this Billera and Diaconis paper, the authors focus on the finite state space case $X$ and considers the MH kernel as the projection of stochastic matrices (row sums are all 1 and all entries are non-negative, denoted by$\mathcal{s}(X)$) onto the set of $\pi$-reversible Markov chains (stochastic matrices that satisfy detailed balance $\pi(x)M(x,y) = \pi(y)M(y,x)$, denoted by $R(\pi)).$ If we introduce a metric on the stochastic matrices: $d(K,K') = \sum_{x} \sum_{x\not=y} \pi(x) |K(x,y)-K'(x,y)|$.

The key result in this paper is Theorem 1. The authors prove that the Metropolis maps $M := M(K)(x,y) = \min\left( K(x,y), \frac{\pi(y}{\pi(x)}K(y,x)\right)$ minimizes the distance $d$ from the proposal kernel $K$ to $R(\pi).$ This means that $M(K)$ is the unique closest element in $R(\pi)$ that is coordinate-wise smaller than $K$ on its off-diagonal entries. So $M$ is in a sense the closest reversible kernel to the original kernel $K$.

I think this geometric interpretation offers great intuition about how the MH algorithm works: we start with a kernel $K$ and change it to another kernel with stationary distribution $\pi$. And the change must occur as follows:

from $x$, choose $y$ from $K(x,y)$ and decide to accept $x$ or stay at $y$; this last choice may be stochastic with acceptance probabilty $F(x,y) \in [0,1]$. This gives the new chain with transition probabilities: $K(x,y) F(x,y)$, x \not =y$. The diagonal entries are changed so that each row sums to 1. Indeed the above procedure describes how the MH algorithm works. If we insist on $\pi$-reversibility, we must have $0 \leq 0 \leq \min(1,R(x,y)$ where $R(x,y) = \frac{\pi(y)K(y,x)}{\pi(x)K(x,y)}.$ So the MH choice of APF is one that maximizes the chance of moving from $x$ to $y$. The resulting MH kernel $M$ has the largest spectral gap (1 – second largest eigenvalue) and by Peksun’s theorem must have the minimum asymptotic variance estimating additive functionals. In Remark 3.2, the authors point out if we consider only APF that are functions of $R(x,y)$, then the function must satisfy $g(x) = x g(1/x)$ which is the characteristic of balancing functions in Zanella’s ‘informed proposals’ paper. This paper allows me to study Metropolis-Hastings algorithm from another angle and review facts I have neglected in my coursework. References: • Billera, L. J., & Diaconis, P. (2001). A geometric interpretation of the Metropolis-Hastings algorithm. Statistical Science, 335-339. • Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424. • Liu, J. S. (2008). Monte Carlo strategies in scientific computing. Springer Science & Business Media. # SRN – Informed proposals for local MCMC in discrete spaces by Zanella (Part II) This is a super delayed Sunday Reading Notes post and today I come back to finish discussing the informed proposals paper by Giacomo Zanella. In Part I of my discussions, I reviewed point-wise informed proposals and locally-informed criterion. In Part II, I hope to focus on asymptotical optimality of locally informed proposals and their simulation studies. The author uses Peskun ordering to compare efficiency of MH schemes. He deduces that ‘locally-balanced proposals are asymptotically optimal in terms of Peskun ordering’ as dimensionality of the underlying state space increases. Conditions for asymptotic optimality are indeed mild (Proposition 1) for the three illustrative examples: 1) independent binary components, 2) weighted permutation and 3) Ising model. In Section 4, Zanella points out a connection between the balancing function and acceptance probability function (APF) for MH algorithms, which I find very interesting. He also shows that the optimal proposal for independent binary variables is Barker choice $g(t) = \frac{t}{1+t}.$ The proof goes by finding the limiting continuous-time process of the MH chain and finding the optimal $g$ for the limiting process. The simulation studies use the illustrative examples: weighted permutations and Ising models. The comparisons are in terms of 1) acceptance rate, 2) number of successful flips per computation time and 3) mixing of some summary statistics. The second criterion concerns the trade-off between computational cost (of calculating the informed proposal) and statistical accuracy (by producing efficient moves). For simple target distributions (such as Uniform), using locally-balanced proposals does not bring much benefits but it achieves a much higher number of flips per unit time for more complicated and ‘rough’ targets. See second row of Figure 1 of the paper. I find it interesting that globally-balanced proposals (aka ‘naively-informed’ or $g(t) = t$) are extremely sensitive to initialization. Looking at the effective sample size (ESS) per time, the chains from GB has much more stable behavior if initialized from stationarity. See GB v.s. GB( station.) in Figure 2. But in Figure 5, this phenomenon does not show up for Ising models: initializing from stationarity does not yield ESS performance comparable to that of LBs. In the simulation studies section, the author emphasizes the cost-vs-efficiency trade-off, which I find very important. I feel I have ignored this aspect of designing MCMC algorithms and should think more about it in my future studies. The author indicates that ‘computations required to sample from locally-balanced proposals are trivially parallelizable’. This is also something very interesting to me and I hope to learn more about multi-core computations during my PhD. In his discussions, the author makes reference to Multiple-Try Metropolis. The connection between LB proposals and MT proposals is not entirely obvious to me, but I intuitively agree with the author’s comment that the weight function in MT serves a very similar purpose as the balancing function term $g\left(\frac{\pi(y)}{\pi(x)}\right)$ in LB. Side note: From my downloaded version of the paper, the transition kernel of the first equation on Page 1 is wrong, because the $Q(x,dy)$ terms needs to be multiplied by its acceptance probability $a(x,y).$ References: • Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424. • Liu, J. S., Liang, F., & Wong, W. H. (2000). The multiple-try method and local optimization in Metropolis sampling. Journal of the American Statistical Association95(449), 121-134. # SRN – Informed proposals for local MCMC in discrete spaces by Zanella (Part I) This week I am reading ‘Informed proposals for local MCMC in discrete spaces‘ by Giacomo Zanella. This paper is about designing MCMC algorithms for discrete-values high-dimensional parameters, and the goal is similar to the papers discussed in previous posts (Hamming ball sampler & auxiliary-variable HMC). I decide to split the Sunday Reading Notes on this paper into two parts, because I find many interesting ideas in this paper. In this paper, Zanella come up with locally-balanced proposals. Suppose $\pi(x)$ is the target density and $K_{\sigma}(x,dy)$ is an uninformed proposal. We assume that as $\sigma \to 0$ the kernel $K_{\sigma}(x,dy)$ converges to the delta measure. Zanella seeks to modify this uninformed proposal so that it incorporates information about the target $\pi$ and is biased towards areas with higher density. An example of locally-balanced proposals is $Q_{\sqrt{\pi}} (x,dy) = \frac{\sqrt{\pi(y) }K_{\sigma}(x,dy)}{(\sqrt{\pi} * K_{\sigma})(x)}$. This kernel is reversible with respect to $\sqrt{\pi(x)}(\sqrt{\pi} * K_{\sigma})(x)$, which converges to $\pi(x)dx$ as $x \to 0.$ [Note the normalizing constatn is the convolution $\sqrt{\pi(x)}* K_{\sigma} = \int \sqrt{\pi(y)} K_{\sigma}(x,dy)].$] More generally, Zanella considers a class of pointwise informed proposals that has the structure $Q_{g,\sigma} = \frac{1}{Z_{g}}\cdot g\left(\frac{\pi(y)}{\pi(x)}\right) K_{\sigma}(x,dy).$ It is suggested that the function $g$ satisfy $g(t) = t g(1/t).$ I will save the discussion on locally-balanced proposals and Peskun optimality to Part II. In this part, I want to discuss Section 5: Connection to MALA and gradient-based MCMC. In continuous space, the point-wise informed proposal $Q_{g,\sigma}$ would be infeasible to sample from because of the term $g\left(\frac{\pi(y)}{\pi(x)}\right) .$ If we take a first-order Taylor expansion, we would have $Q_{g,\sigma}^{(1)} \propto g \left( \exp ( \nabla \log \pi(x) (y-x)) \right) K_{\sigma}(x,dy).$ If we choose $g(t) = \sqrt{t}$ and $K_{\sigma}(x,\cdot) =N(x,\sigma^2)$, this is the MALA proposal. I find this connection very interesting, although I do not have a good intuition about where this connection comes from. One way to explain it is that gradient-based MCMC in continuous space is using local information to design informed proposals. In the conclusions, the author mentions that this connection should improve robustness of gradient-based MCMC schemes and help with parameter tuning. References:(x) • Zanella, G. (2017). Informed proposals for local MCMC in discrete spaces. arXiv preprint arXiv:1711.07424. # SRN – The Hamming Ball Sampler by Titsias and Yau My Sunday Reading Notes (SRN) this semester will mostly be about Bayesian Computations. This week’s post is on The Hamming Ball Sampler proposed by Titsias and Yau.The hamming ball sampler is a MCMC algorithm for high-dimensional discrete-valued vectors or matrices. While reading this paper, I also found a blog post about it from Xi’an’s OG, which provided some high-level intuitions and background knowledge. The paper considers a state space model with discrete hidden space $X$ with parameters $\theta$ and observations $y.$ Factorial Hidden Markov Model (fHMM) is an example of such a model. In state space models, the complete data likelihood can be factorized with $p(y,X,\theta) = p(X,\theta) \prod_{i=1}^N p(y_i|X,\theta).$ Given some prior, we want to sample from the posterior distribution $X,\theta | y.$ When the dimension of $X$ is large, we would suffer from ‘the curse of dimensionality’. Using a Gibbs sampler, we can iteratively sample $\theta \sim \cdot | X,y$ and$\theta X \sim \cdot | \theta,y$. Because the dimension of$X\$ is high, we should also consider blocked Gibbs sampling on $X$ by for example updating one row (or column) of $X$ at a time. While this is conceptually straightforward and potentially also easy to implement, as the authors pointed out:

Conditional sampling may lead to an inability to escape from local modes in the posterior distribution particularly if the elements of $X$ exhibit strong correlations with each other and together with $\theta$.

The Hamming Ball Sampler (HBS) introduces an auxiliary variable $U$ that has the same dimension as the latent space $X$. The augmented joint probability can be factorized with as $p(y,X,\theta,U) = p(U|X) p(y,X,\theta).$ The conditional distribution $p(U|X)$ is chosen to be uniform over a neighborhood set $\mathcal{H}_m(X).$ This set $\mathcal{H}_m(X)$ is a Hamming Ball and it basically says that if $U,X$ are $K \times N$ matrices, then $U$ and $X$ can be different on at most $m$ positions among the $K$ rows. With the auxiliary variable $U$, the Hamming Ball Sampler alternate between the steps $U \gets p(U|X)$ and $(\theta, X) \gets p(\theta,X|U,y).$

The Hamming Ball Sampler is like slice-sampling in discrete spaces, and each Hamming Ball $\mathcal{H}_m(X)$ is a slice. Introducing the slice introduces random exploration, and makes it easier to escape from local modes. For the simplest example where $X$ is a $K \times N$ matrix and the hamming distance is defined the the number of different elements each column, if we set $m = K/2$ then we can potentially change all elements of $X$ in one update. But when $m$ is large, the algorithm complexity also increases.

In this paper the authors provided several numerical examples comparing the Hamming Ball Sampler with block Gibbs Samplers. In the fHMM examples (Figure 4 in the paper) we can see that HBS with $m = 2$ or $3$ achieves having joint posterior density much faster than the block Gibbs Samplers. They also conclude that HB-2 is best balances computational time and sampling efficiency.

Reference:

Titsias, M. K., & Yau, C. (2017). The Hamming ball sampler. Journal of the American Statistical Association112(520), 1598-1611.

Ghahramani, Z., & Jordan, M. I. (1996). Factorial hidden Markov models. In Advances in Neural Information Processing Systems (pp. 472-478).